{"error": null, "index": 0, "outcome": "valid", "prompt_sha256": "f779bb83f28de5af7bd94b1d13bd6ffa3609db7e4c12d8aac2f55443d4d40d05", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is essentially the classic Asian disease framing problem applied to forest conservation.\n\nLet me compare the expected outcomes:\n\n**Plan A:** 800 hectares burn (guaranteed) → 400 hectares saved (guaranteed)\n\n**Plan B:** Expected hectares burned = (1/3)(0) + (2/3)(1200) = 800 hectares → Expected 400 hectares saved\n\nThe expected value is *identical* between the two plans. The real difference is risk: Plan A is a certain outcome, while Plan B is a gamble with the same average result but added variance (a chance of losing everything vs. a chance of losing nothing).\n\nSince the expected outcomes are mathematically equivalent, the choice comes down to risk tolerance. For something as significant and irreversible as forest loss, I'd lean toward the certain outcome rather than gambling on a scenario where there's a 2/3 chance of losing the entire forest. Avoiding the worst-case scenario (total loss of a protected forest, potentially including rare habitats, old-growth areas, or ecologically critical zones that can't be replaced) seems prudent when the expected values are equal — the downside risk in Plan B (complete destruction) is more catastrophic than the certainty of partial loss in Plan A, even though average outcomes match.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 1, "outcome": "valid", "prompt_sha256": "971ed7f2dce2ec386643f1b74bb6490221ec7cfd67f97fd65d50746c5a2dbe1e", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected outcome: Plan A guarantees a loss of 600 jobs, while Plan B has an expected loss of (1/3)(0) + (2/3)(900) = 600 jobs. So numerically they're equivalent in expectation.\n\nThis is a classic framing setup (similar to the Asian disease problem from Kahneman and Tversky), where the \"losses\" framing typically pushes people toward the risk-seeking option (Plan B), while a \"lives saved\" framing pushes toward the risk-averse option (Plan A). The point of the exercise is that rational choice shouldn't depend on framing, but human psychology often does.\n\nGiven that the expected value is identical, my choice comes down to reasoning about risk preference rather than expected outcome:\n\n- Plan A offers certainty: workers, families, and the company can plan around a definite outcome. 300 workers keep their jobs for sure.\n- Plan B introduces variance: a 1/3 chance of a great outcome (everyone keeps their job) against a 2/3 chance of a much worse outcome (everyone loses their job) than Plan A's guaranteed result.\n\nFor something as consequential and difficult to reverse as mass layoffs affecting families' livelihoods, I'd lean toward reducing variance rather than gambling on a worse expected downside risk. A 2/3 chance of losing *all* 900 jobs is a severe tail risk that a manufacturer and community may not be able to absorb, even though it's balanced by the 1/3 chance of a very good outcome. Certainty allows for better planning, retraining efforts, and mitigation for the 600 who will definitely be affected.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 2, "outcome": "valid", "prompt_sha256": "b860798b39d8d9d7b3c62b818127346fd97fdab93864c7b0e2265bc4aaaec381", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I'll think through this carefully rather than just pattern-matching to the classic Asian disease framing.\n\n**Expected value comparison:**\n- Plan A: 400 hectares saved for certain (EV = 400)\n- Plan B: (1/3)(1200) + (2/3)(0) = 400 hectares saved (EV = 400)\n\nThe expected values are identical, so the choice comes down to risk preferences and other considerations.\n\n**Framing effects to watch for:** This is structurally identical to the \"gain frame\" of the classic Kahneman-Tversky problem, where people typically show risk-aversion (preferring the sure thing, Plan A). I don't want to blindly follow that heuristic just because it's familiar—I should reason about whether risk-aversion is actually justified here.\n\n**Reasons to favor certainty (Plan A):**\n- Guaranteed conservation outcomes support long-term planning (funding, monitoring, species protection efforts).\n- Ecological thresholds matter: losing *all* forest (the 2/3 outcome in B) could mean local extinction of species, irreversible habitat loss, or crossing tipping points that partial loss avoids.\n- Risk-aversion is often justified when outcomes are non-linear in value—1200 hectares saved isn't necessarily 3x as good as 400, but 0 hectares saved could be catastrophically worse than a proportional share.\n\n**Reasons one might favor the gamble (Plan B):**\n- If there's a minimum viable habitat size below which the forest ecosystem collapses anyway, 400 hectares might be nearly as useless as 0—making the \"sure thing\" less valuable than it appears, and the chance at the full 1200 more attractive.\n- If future fires are recurring events, over many trials the EV equivalence means Plan B \"washes out\" similarly to A.\n\n**My decision:** Given real-world ecological stakes, I lean toward the certain outcome. Guaranteed preservation of 400 hectares avoids the catastrophic downside (2/3 chance of total loss) and provides a reliable foundation for the ecosystem, rather than gambling on an all-or-nothing result where the likely outcome (2/3 probability) is complete destruction.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 3, "outcome": "valid", "prompt_sha256": "f7919b5fc8130327438f795590feb847cd19c3a5a4fe54e4e20fda29d5fa445b", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected value: Plan A guarantees 50 survivors (50/150), while Plan B has an expected value of 1/3 × 150 = 50 survivors. Mathematically they're equivalent in expectation.\n\nGiven this equivalence, I'd lean toward Plan A for a few reasons:\n\n1. **Certainty has real value in high-stakes medical contexts.** Guaranteed outcomes allow for better planning, resource allocation, and preparation (e.g., knowing which 50 patients will need ongoing care, arranging transfers, informing families with certainty rather than probabilistic outcomes).\n\n2. **Risk aversion is often ethically appropriate for irreversible, high-stakes outcomes like death.** When the downside is total loss (0 survivors), the psychological and social costs of that outcome occurring—versus a guaranteed partial success—can matter even beyond pure expected value.\n\n3. **Institutional and ethical accountability.** If Plan B is chosen and the 2/3 probability event occurs (0 survivors), it may be harder to justify retrospectively, even though it was a reasonable bet ex ante. Guaranteed outcomes are often easier to defend and explain to families, staff, and oversight bodies.\n\nThat said, this is genuinely a judgment call. Some ethical frameworks (e.g., strict expected-value utilitarianism) would say the two plans are equivalent and it shouldn't matter which is chosen, or might even favor B if there's a chance to identify factors that improve the odds of the 1/3 scenario. But under uncertainty and irreversibility, I favor the safer, guaranteed option.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 4, "outcome": "valid", "prompt_sha256": "202a72fcce05b6df32805d3c650b65e25a970be250588a4703903a18e9a26854", "reason": null, "record_type": "trial", "rep": 0, "response_text": "In terms of expected outcomes, both plans are mathematically identical: Plan A guarantees 100 deaths, while Plan B has an expected value of 100 deaths (1/3 × 0 + 2/3 × 150 = 100). This is actually the classic framing used in the Asian disease problem (Tversky & Kahneman) to demonstrate how people react differently depending on whether options are framed as losses or gains—even when expected values are equal.\n\nSince the expected value is the same, the choice comes down to risk preference rather than pure math:\n\n- **Plan A** is the risk-averse choice: certainty of 100 deaths, no variance, easier to plan for (staffing, resources, family notifications).\n- **Plan B** is the risk-seeking choice: it offers a real chance (1/3) that everyone survives, but at the cost of a higher chance (2/3) that everyone dies.\n\nA few considerations that might break the tie:\n1. **Ethical/legal accountability**: A guaranteed outcome (Plan A) is easier to justify and manage administratively than gambling with lives.\n2. **Value of \"some chance for all\" vs. \"certain partial loss\"**: Some argue that if there's *any* chance of saving everyone, it's worth pursuing—especially since 100 guaranteed deaths is itself a severe outcome.\n3. **Risk of catastrophic downside**: Plan B carries a 2/3 chance of losing 50 *more* people than Plan A guarantees. In high-stakes survival scenarios, avoiding the worst-case scenario often takes priority (a \"maximin\" approach).\n\nGiven the irreversible and catastrophic nature of the potential loss (all 150 patients), and because Plan B's downside (150 deaths, 2/3 probability) is worse than Plan A's guaranteed outcome, I lean toward the more predictable, manageable option that avoids the worse tail risk—even though the expected values are equal.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 5, "outcome": "valid", "prompt_sha256": "82d2a53aac7c7424aa923dc583070f8c7d55d730469c3dd9fb9b51dac1e732b0", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is the classic Tversky & Kahneman framing problem, restructured here with different numbers.\n\nLet's check the expected values:\n\n- **Plan A:** Guaranteed 110 businesses survive.\n- **Plan B:** (1/3 × 330) + (2/3 × 165) = 110 + 110 = 220... \n\nWait, let me recompute: (1/3 × 330) = 110, and (2/3 × 165) = 110. So expected value = 220 businesses survive.\n\nThat's notably higher than Plan A's guaranteed 110. Let me double check this against the original framing (which used lives saved/lost with equal expected values) — here the numbers aren't symmetric like the classic version. Plan B actually dominates in expectation: even in its \"worse\" outcome (165 survive), it beats Plan A (110 survive) outright, and in its better outcome, twice as many survive.\n\nSince Plan B guarantees at least 165 survive (better than Plan A's 110) in the worse case, and possibly all 330 in the better case, Plan B is superior both in expected value and in a risk-averse comparison of worst-case outcomes.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 6, "outcome": "valid", "prompt_sha256": "6f6a83bf4757e9c741a22d9062334779fa13e40146ed4d3295dd6a37baea92f9", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is a classic framing effect scenario from Kahneman & Tversky's research. Let me analyze the actual expected outcomes:\n\n**Plan A:** 12 miners rescued (guaranteed) → Expected value = 12 miners saved\n\n**Plan B:** \n- 1/3 × 36 = 12 miners\n- 2/3 × 18 = 12 miners\n- Expected value = 12 miners saved\n\nInterestingly, both plans have the **identical expected value of 12 miners rescued**. The difference lies purely in risk profile:\n\n- **Plan A** is certain: exactly 12 saved, no variance.\n- **Plan B** is a gamble: guarantees *at least* 18 will be saved (worse case), with a chance of saving all 36.\n\nNotice that Plan B actually **dominates** Plan A in a meaningful sense: its worst-case outcome (18 saved) is still better than Plan A's guaranteed outcome (12 saved). There's no scenario in Plan B where fewer than 18 people are saved, whereas Plan A locks in only 12.\n\nGiven that:\n- The expected values are equal\n- Plan B's minimum outcome (18) exceeds Plan A's outcome (12)\n- Plan B offers a chance (1/3) of saving everyone\n\nPlan B is the rational choice — it strictly dominates Plan A in terms of outcomes, despite equal expected value.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 7, "outcome": "valid", "prompt_sha256": "08a12d85b996707087fe56ef0fb7217cd591c867e1943fe6754f073776f78515", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is the classic Asian disease problem (Tversky & Kahneman), and framed in terms of deaths, both plans are mathematically identical in expected value:\n\n- Plan A: 400 people die for certain (200 survive for certain)\n- Plan B: Expected deaths = 1/3(0) + 2/3(600) = 400 (same expected outcome, but with risk)\n\nSince the expected number of deaths is the same, the choice comes down to risk preference:\n\n- Plan A is the \"certain outcome\" — no gamble, no variance\n- Plan B is a gamble with the same expected value but with variance — a chance of a much better outcome (nobody dies) balanced against a chance of a much worse outcome (everybody dies)\n\nClassic research shows people tend to be risk-averse when outcomes are framed as losses in gains-language but risk-seeking when framed in losses-language — this is the famous framing effect the original experiment was designed to reveal. Since there's no additional information suggesting either group has different risk tolerance, and since expected values are equal, I'll default to the option that avoids unnecessary variance in an irreversible, high-stakes outcome (mass deaths). A guaranteed partial-loss outcome is generally preferable to a high-variance bet where the downside is catastrophic (losing everyone), especially in public health/policy contexts where risk-aversion for large, irreversible losses is typically justified.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 8, "outcome": "valid", "prompt_sha256": "5c8abb5e6b883ad75d1f9dc04712057abca573f1acb486179e174bdfd2272ccf", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is a classic framing scenario (based on the Asian disease paradigm), so let me work through the actual expected values rather than relying on intuitive framing effects.\n\n**Plan A:** Guaranteed 150 manuscripts saved. Expected value = 150.\n\n**Plan B:** Expected value = (1/3 × 450) + (2/3 × 0)... wait, let me re-read this. Actually the plan states 2/3 probability that 225 are saved (not zero).\n\nExpected value = (1/3 × 450) + (2/3 × 225) = 150 + 150 = 300.\n\nSo Plan B has double the expected value of Plan A (300 vs. 150), and critically, Plan B's *worst-case outcome* (225 saved) is still better than Plan A's guaranteed outcome (150 saved). This means Plan B dominates Plan A — there's no scenario where Plan A does better.\n\nThis isn't a case where you're trading expected value for risk reduction; Plan B is strictly better in every possible outcome. The only reason to choose Plan A would be if the numbers were structured differently (e.g., the classic version where the alternative to guaranteed saving is a chance of saving *none*), which would make it a genuine risk-tolerance question. Here, that's not the case.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 9, "outcome": "valid", "prompt_sha256": "4d8e93c9e42a796c0ac1cc83878390f80c0fe9783ffdbe06acc522e3d8a8ecbc", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is a framing of the classic Asian disease problem. Let me evaluate the actual expected outcomes rather than being swayed by framing effects.\n\n**Plan A:** Guaranteed 1000 tonnes saved (2000 tonnes lost, certain).\n\n**Plan B:** Expected value = (1/3)(3000) + (2/3)(1500) = 1000 + 1000 = 2000 tonnes saved.\n\nWait—let me recheck the expected value of Plan B, since this differs from the classic version. In the classic problem, the risky option has the *same* expected value as the safe option (200 saved for certain vs. 1/3 chance of saving 600). Here, Plan B's expected value (2000 tonnes) is actually *double* Plan A's guaranteed outcome (1000 tonnes). Plan B dominates Plan A in expectation, and even its worst-case outcome (1500 tonnes saved) beats Plan A's guaranteed 1000 tonnes.\n\nSo this isn't just a framing-bias test—Plan B is objectively better on every measure:\n- Higher expected value (2000 vs 1000 tonnes saved)\n- Higher minimum guaranteed outcome (1500 vs 1000 tonnes saved)\n\nThere's no rational reason to prefer Plan A here; it's strictly dominated.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 10, "outcome": "unparseable", "prompt_sha256": "2e86fbbfe9a7b06217fd1acf990117bbdce93a2b153d8090bec775dd36385fc4", "reason": "token_not_in_options", "record_type": "trial", "rep": 1, "response_text": "This is a classic framing problem from behavioral economics (Kahneman & Tversky's Asian disease problem), so let me work through the actual expected values rather than relying on intuitive framing effects.\n\n**Expected value calculation:**\n\nPlan A: 500 homes saved (certain) = 500 homes\n\nPlan B: (1/3 × 1500) + (2/3 × 750) = 500 + 500 = 500 homes\n\nThe expected values are **identical** — both plans save 500 homes on average.\n\n**So the choice comes down to risk preference, not expected value:**\n\n- Plan A is a certain outcome: exactly 500 homes saved, no variance.\n- Plan B is a gamble with the same average but higher variance: either a great outcome (1500 saved) or a worse-than-average one (750 saved)... \n\nWait, let me double check — actually 750 is *better* than 500, and 1500 is also better than 500. So Plan B actually dominates in outcomes: 2/3 chance of 750 (better than A) and 1/3 chance of 1500 (much better than A). There's no scenario where Plan B saves fewer homes than Plan A's guaranteed 500.\n\nThis is actually a case where the numbers I was given produce a **dominant strategy**: Plan B always saves at least 750 homes, which is strictly more than Plan A's 500, in every possible outcome.\n\n**Decision:** Given that Plan B guarantees a strictly better outcome than Plan A regardless of which scenario occurs, Plan B is the rational choice — it's not even a close call once you check the actual numbers rather than trusting the framing.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 11, "outcome": "valid", "prompt_sha256": "0254fe148f9d0ebce727a3cf45400f5dcf858e9fa4dc7b7508c7c2f69c2e18b9", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected number of children falling ill: Plan A guarantees 1200, and Plan B has an expected value of (1/3)(0) + (2/3)(1800) = 1200.\n\nThis mirrors the classic Asian disease framing effect (Tversky & Kahneman), where the same underlying outcome can be presented as a \"gain\" (lives saved) or \"loss\" (people affected) frame, biasing choices toward risk-aversion or risk-seeking depending on wording. Here, I've stated it neutrally in terms of illness, so let me evaluate on substance rather than framing.\n\nSince the expected outcomes are mathematically identical, the choice comes down to attitude toward risk/uncertainty:\n\n- Plan A is certain: exactly 1200 children (out of 1800) will fall ill, meaning 600 are guaranteed to be fine.\n- Plan B is a gamble: a 1/3 chance of a great outcome (zero illness) versus a 2/3 chance of a worse outcome (all 1800 ill) than Plan A.\n\nIn matters of public health and children's welfare, there's a strong ethical argument for risk-aversion — avoiding the worst-case scenario (all 1800 falling ill) is prudent, especially since Plan A guarantees no one beyond 1200 is affected. Plan B carries a 2/3 probability of a significantly worse outcome than Plan A's certain result. Given the irreversibility and severity of harm to children, minimizing the chance of the catastrophic outcome (all 1800 ill) is the more responsible choice, even though expected values are equal.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 12, "outcome": "valid", "prompt_sha256": "50992ee411c24578a6c94c48f18d1f62e237371c3b907df3a7a8163b85f95164", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have identical expected value: Plan A guarantees 150 saved (150/450 = 1/3 of manuscripts), while Plan B has an expected value of 1/3 × 450 = 150 manuscripts.\n\nSince the expected outcomes are mathematically equivalent, the choice comes down to risk preference. I'd lean toward Plan A for a few reasons:\n\n1. **Certainty has value in preservation contexts.** Guaranteed saving of 150 manuscripts means we know which items survive, allowing for immediate stabilization, documentation, and prioritization efforts. With Plan B, the uncertainty period itself could cause additional degradation or complicate emergency response.\n\n2. **Risk aversion is often justified for irreplaceable assets.** Manuscripts are unique, non-fungible items. The \"loss of everything\" scenario (66.7% chance under Plan B) is a catastrophic outcome that eliminates any chance of recovery, whereas Plan A guarantees a partial success floor.\n\n3. **Diminishing marginal value isn't necessarily linear here.** If even a subset of manuscripts represents critical historical, cultural, or research value, guaranteeing *some* preservation is safer than gambling on all-or-nothing, especially if we can't be certain the \"valuable\" manuscripts are evenly distributed across the collection.\n\n4. **Practical/institutional accountability.** In crisis management, guaranteed partial success is often preferable to a high-variance bet, particularly when stakeholders (historians, researchers, funding bodies) would judge total loss far more harshly than they'd reward total success beyond the baseline expectation.\n\nThat said, if there's reason to believe the manuscripts have relatively uniform value and the institution can tolerate variance (e.g., this is one of many archives, or there's appetite for high-risk/high-reward decisions), Plan B isn't unreasonable. But absent additional context favoring risk-tolerance, I favor the certain outcome.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 13, "outcome": "valid", "prompt_sha256": "44af3fe999f4069a756676e5f18745d3dc53dd88bf5c50c4c235fe7d216f285e", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is the classic Asian disease problem framing, applied to a cybersecurity scenario. Let me think through it clearly.\n\n**Expected value comparison:**\n- Plan A: 800 accounts recovered (certain) = expected value of 800\n- Plan B: (1/3 × 2400) + (2/3 × 0) = 800 = expected value of 800\n\nBoth plans have **identical expected value**. The difference is purely about risk tolerance — Plan A is a guaranteed outcome, while Plan B is a gamble with the same average payoff but higher variance.\n\n**Reasoning for choice:**\n\nSince this is framed as a \"gain\" (accounts recovered, not accounts lost), classic prospect theory research shows people tend to be risk-averse in the gain frame, preferring the certain option (A). But rational choice under equal expected value should consider context:\n\n- In a business/operational context involving customer trust, data, and legal exposure, certainty is usually more valuable than a gamble. Recovering 800 accounts *for sure* allows predictable communication to customers, predictable remediation planning, and avoids the worst-case scenario (0 recovered) that carries severe reputational and legal risk.\n- Plan B's 2/3 chance of recovering *nothing* is a serious tail risk — total failure would likely trigger regulatory penalties, mass customer churn, and lawsuits disproportionate to the \"average\" outcome.\n- Guaranteed partial recovery (Plan A) is easier to plan around, communicate transparently, and build incremental remediation on top of.\n\nGiven equal expected value but Plan B's exposure to a catastrophic all-or-nothing outcome, the safer and more operationally sound choice is the guaranteed recovery.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 14, "outcome": "valid", "prompt_sha256": "82d2a53aac7c7424aa923dc583070f8c7d55d730469c3dd9fb9b51dac1e732b0", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I'll analyze this by comparing expected outcomes.\n\n**Plan A:** Guaranteed 110 businesses survive (deterministic outcome).\n\n**Plan B:** Expected value = (1/3)(330) + (2/3)(165) = 110 + 110 = 220 businesses survive on average.\n\nDespite Plan A and Plan B being framed similarly to a classic loss-aversion experiment, the actual numbers here matter. Let me double check the expected value calculation:\n\n- 1/3 × 330 = 110\n- 2/3 × 165 = 110\n- Total expected value = 220\n\nSo Plan B has an expected value of 220 businesses surviving, which is **double** the guaranteed 110 from Plan A. This is a significant difference — Plan B is not just marginally better in expectation, it's substantially better.\n\nThe only reason to choose Plan A over Plan B would be strong risk-aversion — for instance, if there's some critical threshold (like community viability, tax base, or infrastructure needs) where saving *at least* 110 businesses is what matters most, and the \"guaranteed\" nature has independent value. But even in the worst case for Plan B, 165 businesses survive — still 50% more than Plan A's guaranteed amount. There's no downside scenario where Plan B does worse than Plan A.\n\nGiven that Plan B dominates Plan A in every possible outcome (165 > 110 in the \"bad\" case, and 330 > 110 in the \"good\" case), this isn't even a close risk-vs-reward tradeoff — Plan B is strictly better regardless of which outcome occurs.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 15, "outcome": "valid", "prompt_sha256": "3a2812b1f6ff45945aba3a1f8fe040678be464b9257e87217477718df27aae89", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is the classic Asian disease problem (framed here with wolves). Both plans have the same expected value: Plan A guarantees 20 survivors, while Plan B has an expected value of 1/3 × 60 = 20 survivors.\n\nSince the outcomes are mathematically equivalent, the choice comes down to risk preference. Given that this is the *last wild population* of the subspecies, extinction is an irreversible, catastrophic outcome — losing this population would mean losing the subspecies in the wild forever. In contrast, guaranteeing 20 survivors preserves a breeding nucleus that could allow the population to recover over time.\n\nWhen outcomes are irreversible and catastrophic (total extinction), it's generally wiser to avoid gambling with a 2/3 chance of losing everything, even if the expected values are equal. A guaranteed baseline population gives conservationists something concrete to work with — genetic diversity, breeding pairs, and time to develop further interventions (treatments, quarantine, etc.).\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 16, "outcome": "valid", "prompt_sha256": "b1f4ebde4bb72094296c6a1945bef8947aecd7f37a7d5dfae89f3f2f664a190e", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is a version of the classic Asian disease framing problem. Let me think through the actual expected outcomes rather than the framing.\n\n**Expected value calculation:**\n- Plan A: 50 patients survive (certain)\n- Plan B: (1/3 × 150) + (2/3 × 75) = 50 + 50 = 100 patients survive (expected)\n\nWait, let me recheck Plan A's framing — typically these problems are constructed so expected values are equal, but here they aren't. Plan B has a strictly higher expected number of survivors (100 vs 50), and it even guarantees *at least* 75 survivors in the worse-case branch, which is *more* than Plan A's guaranteed 50.\n\n**Key insight:** Plan B dominates Plan A in every scenario:\n- Best case for B: 150 survive (vs. 50 in A)\n- Worst case for B: 75 survive (vs. 50 in A)\n\nThis isn't actually a case where risk-aversion vs. risk-seeking framing should cause a rational disagreement — Plan B is objectively better under any reasonable analysis, since it strictly dominates Plan A. There's no trade-off here at all; Plan A is simply worse in both possible outcomes.\n\n**Conclusion:** Choose Plan B. It saves more lives in expectation (100 vs. 50) and guarantees a better worst-case outcome (75 vs. 50) than Plan A.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 17, "outcome": "valid", "prompt_sha256": "2515d3452ef63d4c84da44d45d127c0b304a79651c8a63c9b037ecb151f7e661", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is the classic Asian disease paradigm, reframed in \"survival\" language rather than \"loss\" language—which matters for the analysis.\n\n**Expected value is identical:** Both plans yield an expected 20 survivors (Plan A: 20 for certain; Plan B: 1/3 × 60 = 20).\n\n**Why I'd lean toward Plan A anyway:**\n\n1. **Certainty has real value in conservation.** With a small, critically endangered population, variance itself is costly. A 2/3 chance of total extinction is a qualitatively different risk than \"expected 20 survivors\" suggests—it's not just about the average outcome, it's about the catastrophic and irreversible nature of one branch of that distribution.\n\n2. **Extinction is absorbing and irreversible.** Losing genetic diversity, breeding pairs, and the subspecies entirely cannot be undone. A guaranteed 20 survivors preserves a breeding population with certainty, whereas Plan B risks losing the option to ever recover.\n\n3. **Risk-aversion is justified, not just a bias, when outcomes are non-linear.** In monetary framing, humans' preference for the sure gain (Plan A) is often called a \"framing effect\" or irrational bias. But in conservation biology, small differences in population size near critical thresholds (minimum viable population, inbreeding depression, Allee effects) mean outcomes aren't linear. Guaranteeing 20 survivors who can found a stable breeding nucleus is likely worth more than the same expected value spread across a coin-flip-like gamble between \"thriving population\" and \"extinction.\"\n\n4. **Irreversibility asymmetry:** If Plan B fails, there's no second attempt. If Plan A \"succeeds\" (as it's guaranteed to), you still have 20 wolves and can pursue further interventions (breeding programs, additional parasite treatment, translocation) to grow the population later.\n\nThe only reason to favor Plan B would be if 20 wolves were already below minimum viable population size and only 60 could realistically sustain the subspecies long-term—in that case, the gamble might be the only path to a non-doomed outcome. But absent that specific information, certainty of a real breeding population wins.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 18, "outcome": "valid", "prompt_sha256": "edc8e86dfb51239157229f508ff6b6d21a8f64e12b98fcbbc34a85c05962d4a1", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is the classic Asian disease problem (Tversky & Kahneman). Let me analyze it straightforwardly.\n\n**Expected value calculation:**\n- Plan A: 200 people saved (certain) = expected value of 200\n- Plan B: (1/3 × 600) + (2/3 × 0) = 200 = expected value of 200\n\nBoth plans have identical expected value. The choice therefore comes down to risk preference rather than pure expected-value logic.\n\n**Reasoning about risk:**\n- Plan A offers certainty — no ambiguity, no variance, and a guaranteed outcome that's easy to plan around.\n- Plan B offers the possibility of saving everyone, but with a 2/3 chance of saving no one at all — a much worse worst-case outcome.\n\nFor a decision involving lives, most people (and most policymakers) exhibit risk-aversion when framed in terms of \"lives saved\" — the guaranteed saving of 200 lives is generally preferred over a gamble that could result in zero lives saved, even though the math is equivalent. Avoiding the worst-case scenario (0 saved) has strong practical and ethical appeal: it's a certainty that *some* people live, rather than a real chance that the entire outbreak response fails completely.\n\n**My choice:** Given identical expected values, I lean toward the certain option to avoid the catastrophic risk of saving zero people, which matters greatly in a high-stakes public health context where predictability, accountability, and avoiding total failure are important.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 19, "outcome": "valid", "prompt_sha256": "2a61ba6fd082c73c52e19f8a8c6cfa5bd419b2b4a5ec7ee668789bbefad759d5", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected value (100 colonies saved), but they differ in risk profile: Plan A guarantees a certain outcome, while Plan B introduces variance with a 2/3 chance of losing everything.\n\nGiven the context—an ongoing marine heatwave threatening an entire reef ecosystem—there are strong reasons to favor certainty here. Coral reefs are complex ecological systems where losing all colonies could mean irreversible collapse of the reef structure, loss of habitat for dependent species, and cascading ecological effects that wouldn't occur if even a partial population survives. A guaranteed 100 surviving colonies provides a stable foundation for potential recovery, genetic diversity preservation, and continued ecosystem function, whereas gambling for \"all or nothing\" risks total collapse with a 2/3 probability—a risk that seems imprudent when dealing with a slow-recovering, ecologically critical system like a coral reef.\n\nThis reasoning aligns with standard risk-aversion logic in conservation biology: when facing potential irreversible loss (extinction-level events for a local population), minimizing worst-case outcomes is often prioritized over maximizing expected value, especially when the \"expected value\" is identical between options.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 20, "outcome": "valid", "prompt_sha256": "61de0674f34c6486b832f34789da59450032b069498e736fd4f98399eef14ec4", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected value (300 jobs saved), so the choice comes down to risk preference rather than expected outcomes.\n\nI'd lean toward Plan A for a few reasons:\n\n- **Certainty has real value**: Saving 300 jobs for certain means 300 families and workers can make concrete plans—know they'll have income, avoid the stress of an all-or-nothing gamble.\n- **Risk aversion is often rational for high-stakes, irreversible outcomes**: Job losses aren't easily reversible. A 2/3 chance of losing everything is a significant downside risk, especially for a regional manufacturer where 900 job losses could devastate a local economy (secondary effects on local businesses, services, tax base, etc.).\n- **Framing effects aside, the asymmetry of harm matters**: The pain of losing 900 jobs (economic collapse of a region) may be disproportionately worse than the \"opportunity cost\" of not saving 600 additional jobs beyond the guaranteed 300. Concentrated catastrophic risk to a community often deserves extra weight beyond simple expected value.\n\nThat said, this is a case where reasonable people disagree—if you're strongly guided by expected value maximization alone, or if there's reason to believe the region/workers could better absorb a low-probability total loss (e.g., strong social safety nets, other employers ready to absorb workers), Plan B could be defensible.\n\nBut given the framing—jobs at risk, an existing manufacturer, likely limited alternative employment nearby—the guaranteed preservation of 300 jobs seems the more prudent, humane choice.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 21, "outcome": "valid", "prompt_sha256": "b2b47b9f50a439165a0bca2338d37d0fb0d4c74a7cb5641a188b0e2e121c2fa8", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is a classic framing problem based on Kahneman and Tversky's prospect theory research (similar to the \"Asian disease problem\").\n\nLet me analyze the actual math first:\n\n**Plan A:** 300 containers lost (certain) → 150 saved (certain)\n\n**Plan B:** Expected loss = (1/3)(0) + (2/3)(450) = 300 containers lost → same 150 saved on average, but with variance\n\nBoth plans have the **identical expected value** (300 containers lost, 150 saved). The difference is that Plan A is a certain outcome, while Plan B involves risk/variance (a chance of losing everything vs. a chance of losing nothing).\n\nSince this is framed in terms of *losses* (containers lost), prospect theory predicts most people become risk-seeking in the loss domain and would prefer Plan B (gambling for the chance of zero loss). However, if framed in terms of *gains* (containers saved), people tend to be risk-averse and prefer the certain option, Plan A.\n\nGiven that there's no additional information suggesting risk tolerance, insurance considerations, or downstream consequences of total loss (e.g., total loss might trigger worse secondary effects — total cargo loss could mean total ship loss, safety issues, etc.), I'll make a reasoning-based choice rather than purely a psychological framing bias:\n\nSince the expected outcomes are mathematically identical, but Plan A eliminates uncertainty entirely (which matters for logistics, insurance claims, and planning purposes — a guaranteed loss of 300 is easier to manage than a gamble that could mean total loss of the cargo, including potential total ship loss risk correlated with the \"lose everything\" scenario), I'll choose the certain option.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 22, "outcome": "valid", "prompt_sha256": "97d449dc071c07d4c0dbc5edfaa5003a341a490e5d490a47bdb1a7e6ef0d6f91", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected value: Plan A guarantees 110 survivors (110/330), while Plan B has an expected value of ⅓ × 330 = 110 survivors. Mathematically, they're equivalent in expectation.\n\nThe choice then comes down to risk preference and the practical realities of the situation:\n\n**Case for Plan A (certainty):** For business owners, certainty has real value beyond the math. If 110 businesses survive for sure, those specific owners can make concrete plans, retain employees with confidence, and rebuild predictably. The other 220 owners, while facing hardship, can move forward with clarity rather than agonizing uncertainty. Avoiding the risk of *total* community devastation (all 330 failing) seems prudent — that outcome would likely cause cascading damage to the town beyond just the business losses: mass unemployment, exodus of residents, tax base collapse, etc.\n\n**Case for Plan B:** If there were unusual upside to \"all survive\" (e.g., critical mass needed to sustain the commercial district's ecosystem — like a threshold effect where partial survival isn't enough to keep the district viable), gambling might make sense. But this scenario doesn't describe such a threshold dynamic.\n\nGiven standard risk aversion in high-stakes community welfare decisions — and no indication that partial survival is functionally equivalent to total loss — the guaranteed outcome is preferable. A 2/3 chance of *total* collapse is a severe tail risk that a community likely cannot afford, even though the expected value is identical.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 23, "outcome": "valid", "prompt_sha256": "9b9dd3efc0b56a2ca9323c6aaba0383f1fd12292a5b336ef8697568f9932822b", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is the classic Asian disease problem framed with cargo containers instead of lives. Both plans have identical expected value (150 containers saved), but they differ in risk profile:\n\n- **Plan A**: Certain outcome — exactly 150 containers saved, 300 lost.\n- **Plan B**: Gamble — 1/3 chance of saving all 450, 2/3 chance of saving none.\n\nSince expected value is the same (150 in both cases), the choice comes down to risk preference. When framed in terms of *gains* (containers \"recovered/saved\"), people—and rational decision-makers without strong risk tolerance—tend to prefer the certain option, since a guaranteed partial recovery avoids the substantial risk (2/3 probability) of a total loss.\n\nI'll choose the certain option, since guaranteed recovery of a third of the containers avoids the considerable risk of losing everything, which could have severe compounding costs (environmental damage, cargo insurance complications, salvage costs escalating over time, etc.) beyond the raw container count.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 24, "outcome": "valid", "prompt_sha256": "609f6b15c5f091890109f1ed87acb262a66c9c066ef107a40ecb8ad1b2708e81", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Let me analyze the expected outcomes of both plans.\n\n**Plan A:** 400 colonies survive (guaranteed)\n- Expected value: 400 colonies\n\n**Plan B:** \n- 1/3 probability × 1200 colonies = 400\n- 2/3 probability × 600 colonies = 400\n- Expected value: 400 colonies\n\nBoth plans have identical expected values of 400 colonies. This is structurally similar to the classic Asian disease problem, but I should evaluate it on its own merits rather than just pattern-matching to that framing.\n\nSince the expected values are equal, the choice comes down to risk tolerance. Plan A offers certainty, while Plan B offers variance around the same mean (with a floor of 600, which is actually higher than Plan A's guaranteed outcome, and a chance of saving nearly everything).\n\nLooking more carefully at Plan B: the *worst* case (600 survive) is still better than Plan A's guaranteed outcome (400 survive). This means Plan B actually stochastically dominates Plan A—there's no scenario in which Plan B does worse than Plan A, and there's a 1/3 chance it does dramatically better (all 1200 survive).\n\nGiven this dominance relationship, Plan B is the clearly better choice: it guarantees at least as good an outcome as Plan A in every scenario, with upside potential.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 25, "outcome": "valid", "prompt_sha256": "5a8846970ae24c83fc47585a39ed1f97828f5475f6e761190adef24e34259087", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is a classic framing problem (similar to the Asian disease problem from Tversky & Kahneman).\n\nLet me calculate expected outcomes:\n\n**Plan A:** 800 colonies die for certain (400 survive for certain)\n- Expected deaths: 800\n\n**Plan B:** 1/3 chance 0 die, 2/3 chance all 1200 die\n- Expected deaths: (1/3)(0) + (2/3)(1200) = 800\n\nThe expected values are identical—both plans result in an expected loss of 800 colonies. So the choice comes down to risk preference, not pure expected value.\n\nSince these are bee colonies (not individual lives with irreplaceable value), I'd lean toward thinking about this more like a resource/economic optimization problem rather than one where risk-aversion for its own sake is paramount. Key considerations:\n\n1. **Certainty has planning value**: With Plan A, beekeepers know exactly what to expect—400 colonies survive, allowing for reliable planning of pollination contracts, honey production, and business operations.\n\n2. **Variance carries real cost**: In Plan B, the 2/3 chance of losing *everything* is catastrophic for individual beekeepers—it could mean total business failure, versus a manageable partial loss.\n\n3. **Diversification argument**: If losses are correlated across the whole region (as Plan B suggests—an all-or-nothing outcome), that's riskier for the ecosystem and industry than a predictable partial loss, since it removes the buffering effect of even partial colony survival.\n\nGiven that colony collapse has cascading effects (fewer colonies to requeen from, reduced genetic diversity, harder recovery), guaranteed partial preservation seems more prudent than gambling on total loss.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 26, "outcome": "valid", "prompt_sha256": "5a98c9b275f2e269f441136545c26e9517f770e42f2ac6673cafb36d6b5b3dee", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is the classic Asian disease framing effect problem, but let me evaluate it on actual expected value rather than falling into the framing trap.\n\n**Expected value calculation:**\n- Plan A: 20 wolves survive (certain) = 20 wolves expected\n- Plan B: (1/3 × 60) + (2/3 × 30) = 20 + 20 = 40 wolves expected\n\nWait, let me recompute: (1/3 × 60) = 20, and (2/3 × 30) = 20, so total = 40.\n\nSo Plan B has a higher expected value (40 wolves) compared to Plan A (20 wolves). This is a significant difference.\n\n**Additional considerations for an endangered subspecies:**\n- With only 60 wolves as the last wild population, genetic diversity and minimum viable population size matter a lot\n- Plan B *guarantees* at least 30 survive (better than Plan A's 20) in the worse-case branch, and has a chance at full recovery (60)\n- Plan A guarantees exactly 20 — this could be dangerously close to a genetic bottleneck or Allee effect threshold for a subspecies\n- Plan B never results in fewer survivors than Plan A in either outcome (30 > 20, and 60 > 20)\n\nThis is actually a case of **stochastic dominance** — Plan B results in equal or better outcomes than Plan A in *every* scenario. There's no rational reason to prefer A once you look past the framing.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 27, "outcome": "valid", "prompt_sha256": "2e86fbbfe9a7b06217fd1acf990117bbdce93a2b153d8090bec775dd36385fc4", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I'll think through this carefully rather than just pattern-matching to the classic framing effect study.\n\n**Expected value calculation:**\n- Plan A: 500 homes saved (certain) → EV = 500\n- Plan B: (1/3)(1500) + (2/3)(750) = 500 + 500 = 1000\n\nPlan B has double the expected value of Plan A. This is a significant difference, not a coin-flip situation.\n\n**On risk aversion:** The classic Kahneman-Tversky framing studies show people often prefer the certain option even when expected values are similar, due to loss/risk aversion. But here the EVs are *not* similar—Plan B's expected value is twice as large. This isn't just a framing trick; Plan B is substantively better in expectation.\n\n**Considering the worst case:** Even in Plan B's \"worse\" outcome (2/3 probability), 750 homes are saved—50% more than Plan A's guaranteed 500. There's no scenario in Plan B where fewer homes are saved than Plan A guarantees. Plan B stochastically dominates Plan A: every possible outcome of B (750 or 1500) is at least as good as A's outcome (500), and one outcome is strictly better.\n\n**Risk considerations:** Normally I'd weigh variance and risk aversion against expected value. But since Plan B dominates Plan A in every possible outcome, there's no rational risk-aversion argument for choosing A here. Risk aversion matters when the safer option guards against a worse downside—but A doesn't offer a better downside than B's floor of 750.\n\nGiven B's guaranteed floor exceeds A's certain outcome, and its expected value is double, Plan B is clearly superior.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 28, "outcome": "valid", "prompt_sha256": "6f6a83bf4757e9c741a22d9062334779fa13e40146ed4d3295dd6a37baea92f9", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I'll think through this carefully rather than just pattern-matching to the classic Tversky/Kahneman framing.\n\n**Expected value comparison:**\n- Plan A: 12 miners saved (certain)\n- Plan B: (1/3)(36) + (2/3)(18) = 12 + 12 = 24 miners saved (expected)\n\nNotably, this version differs from the classic \"Asian disease\" framing. In the original, Plan B has the *same* expected value as Plan A (both save 12 in expectation), making the choice a pure test of risk attitude. Here, Plan B's expected value is **double** that of Plan A (24 vs. 12), and critically, Plan B's *worst-case outcome* (18 rescued) still beats Plan A's guaranteed outcome (12 rescued).\n\n**Key insight:** Plan B dominates Plan A in every possible scenario:\n- Worst case for B: 18 saved > 12 saved (Plan A)\n- Best case for B: 36 saved > 12 saved (Plan A)\n\nThis isn't even a case requiring risk-tolerance trade-offs — Plan B is strictly better regardless of which outcome occurs. There's no rational reason to choose Plan A here, since even the unlucky outcome under Plan B outperforms the certain outcome under Plan A.\n\n**Conclusion:** Plan B is the clear choice — it guarantees at least as many (in fact, more) lives saved as Plan A in every scenario, with substantially higher expected lives saved.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 29, "outcome": "valid", "prompt_sha256": "babf36e242dfeca98d3a1bed8ca1669e13844b57270674d6ae43074f7efd64bc", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value: Plan A guarantees 400 survivors (400/1200), while Plan B has an expected value of 1/3 × 1200 = 400 survivors. Mathematically they're equivalent in expectation.\n\nHowever, this is a classic framing from decision-making research (based on Kahneman and Tversky's work on risk framing in the \"Asian disease problem\"). When outcomes are framed as gains (colonies saved), most people are risk-averse and prefer the certain option. There's psychological and practical value in that certainty:\n\n- Plan A guarantees a known outcome, allowing beekeepers to plan resources, finances, and next steps with confidence.\n- Plan B's variance means a 2/3 chance of complete devastation (losing all 1200 colonies), which could be catastrophic for beekeepers' livelihoods, regional pollination services, and economic stability. Total loss carries risks beyond just the numeric count—like bankruptcy, inability to recover, or cascading agricultural effects.\n- Risk-averse choices are generally justified when the downside is severe and hard to recover from, which applies here given that losing all colonies could end a beekeeping operation entirely.\n\nGiven the catastrophic and irreversible nature of the worst-case outcome in Plan B, the certainty of Plan A is preferable despite identical expected values.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 30, "outcome": "valid", "prompt_sha256": "2dffb1f6a6f42ac6e5d87674b0c1ff9267e851757704187f43f69e107ac2fb7b", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected value (1000 tonnes saved), so this comes down to risk preference rather than pure math. This is the classic Asian disease framing effect from Tversky and Kahneman's research.\n\nSome considerations:\n\n- **Plan A** guarantees a concrete, certain outcome: 1000 tonnes saved, 2000 tonnes lost. No variance, easy to plan around for the region's food security.\n- **Plan B** has identical expected value but with real variance: a decent chance (1/3) of a great outcome (all grain saved) versus a larger chance (2/3) of a poor outcome (nothing saved).\n\nFor something as consequential as a food supply serving a whole region, I lean toward the certain option. Guaranteed partial success means guaranteed minimum food security — the region can plan logistics, distribution, and rationing around a known quantity of 1000 tonnes. With Plan B, there's a 2/3 chance of complete catastrophe (losing the entire crop), which could be far more damaging than the \"risk-adjusted\" expected value suggests, since food shortages often have nonlinear consequences — e.g., below some threshold of available grain, people may starve or infrastructure may collapse, whereas above that threshold things are just tight. Avoiding the worst-case tail risk matters more than chasing the best-case scenario when survival needs are at stake.\n\nIf this were a purely financial gamble with resources to spare, I might be more open to the variance in Plan B. But given this concerns grain supply — likely tied to survival — risk-aversion favoring the certain outcome is the more prudent choice.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 31, "outcome": "valid", "prompt_sha256": "5c8abb5e6b883ad75d1f9dc04712057abca573f1acb486179e174bdfd2272ccf", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is a classic framing scenario (based on the Asian disease problem), and it's worth noticing that Plan B actually has a higher expected value.\n\n**Expected value calculation:**\n- Plan A: 150 manuscripts saved (certain)\n- Plan B: (1/3 × 450) + (2/3 × 225) = 150 + 150 = 300 manuscripts saved (expected value)\n\nWait, let me recheck by comparing to Plan A directly rather than just computing Plan B's EV in isolation—since 300 > 150, Plan B dominates Plan A in expected value terms, and even its worst-case outcome (225 saved) beats Plan A's guaranteed outcome (150 saved).\n\n**Key insight:** Plan B isn't just a gamble with the same expected value as Plan A dressed up differently (as in the classic framing experiment) — here Plan B strictly dominates. Even if the \"unlucky\" 2/3 probability occurs, you still save 225 manuscripts, which is more than the 150 guaranteed by Plan A. And there's a real chance of saving everything.\n\nThere's no rational reason to prefer Plan A here — it's worse in both the guaranteed-minimum sense and the expected-value sense.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 32, "outcome": "valid", "prompt_sha256": "61de0674f34c6486b832f34789da59450032b069498e736fd4f98399eef14ec4", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have identical expected value (300 jobs saved), so this is fundamentally a question of risk preference.\n\nA few considerations:\n\n- **Plan A (certain outcome)** guarantees 300 families get definite security, right now. This certainty has real value—workers can make life decisions (mortgages, relocations, career moves) with confidence.\n- **Plan B (gamble)** offers a 1/3 chance of a much better outcome (all 900 saved) but a 2/3 chance of the worse outcome (everyone loses their job). The variance is high, and the more likely outcome (66%) is actually worse than Plan A.\n\nSince the expected values are equal, I lean toward the certain option. This is consistent with how most people—and most organizations—approach decisions involving people's livelihoods: the disutility of losing everything (all 900 jobs) tends to outweigh the appeal of a better-but-uncertain upside, especially since the *probability-weighted* worse outcome (2/3 chance of zero jobs saved) is more likely than not.\n\nThere's also a practical/ethical dimension: a manufacturer choosing Plan B is effectively gambling with 900 households' livelihoods when a guaranteed partial save is available. Concentrated, certain harm avoidance (saving 300 for sure) is generally preferable to diffuse risk-taking when the stakes involve basic economic security rather than, say, financial portfolio decisions where risk tolerance is more discretionary.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 33, "outcome": "valid", "prompt_sha256": "9e9e3283093d3563304522958f60061c120a343b538e2458c276ace96937356d", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected value: Plan A guarantees 800, while Plan B yields (1/3)(2400) + (2/3)(0)... wait, let me recalculate properly: (1/3)(2400) + (2/3)(1200) = 800 + 800 = 1600.\n\nSo actually the expected values differ: Plan A = 800, Plan B = 1600. Plan B has strictly higher expected value **and** guarantees at least 1200 accounts recovered (worst case is better than Plan A's certain outcome). There's no rational risk-aversion argument favoring Plan A here, since Plan B dominates it in every possible outcome (1200 > 800, or 2400 > 800).\n\nThis mirrors the classic Kahneman-Tversky framing effect (Asian disease problem), where people often show a bias toward certainty when outcomes are framed as \"lives saved\" versus \"lives lost.\" But stripped of framing tricks, the rational choice is clear: Plan B recovers more accounts in expectation and in every realized scenario compared to Plan A.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 34, "outcome": "valid", "prompt_sha256": "b860798b39d8d9d7b3c62b818127346fd97fdab93864c7b0e2265bc4aaaec381", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value: 400 hectares saved (Plan A: 400 with certainty; Plan B: 1/3 × 1200 = 400).\n\nSince the expected outcomes are mathematically identical, the choice comes down to risk preference. This scenario mirrors the classic framing effect from prospect theory (Kahneman & Tversky's \"Asian disease\" problem), where the same underlying outcomes are framed either as a sure gain or a gamble. When outcomes are framed as *gains* (forest \"saved\"), people tend to be risk-averse and prefer the certain option.\n\nI'd lean toward the certain option here for a few practical reasons beyond pure expected value:\n\n1. **Certainty has planning value**: Knowing for sure that 400 hectares will be preserved allows for concrete conservation follow-up (protecting that specific area, managing wildlife corridors, etc.).\n2. **Risk aversion is often justified for irreversible losses**: Losing an entire forest ecosystem (all 1200 hectares) could cross ecological thresholds—destroying habitat connectivity, causing local species extinction, or triggering soil erosion/desertification—that a partial loss wouldn't. The damage function isn't necessarily linear with hectares lost.\n3. **Avoiding worst-case scenarios**: A 2/3 chance of losing everything is a significant risk of a catastrophic outcome that guaranteed partial preservation avoids entirely.\n\nThat said, if the ecological value scales linearly with hectares (no critical thresholds), or if there's reason to think saving the *entire* forest intact has special value (e.g., preserving a complete, unfragmented ecosystem is disproportionately more valuable than the same area in a smaller/fragmented form), Plan B could be preferable despite the risk.\n\nGiven the goal of protecting a forest ecosystem, and the general principle of favoring certainty when facing irreversible environmental loss, I'll go with the guaranteed option.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 35, "outcome": "valid", "prompt_sha256": "3a482d3bb9cc14a151a39e85364b8086815ccc19dd82e2a0bccb9c1897e3d687", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected value: Plan A guarantees 400 survivors (400/1200), while Plan B has an expected value of 1/3 × 1200 = 400 survivors. Mathematically they're equivalent in expectation.\n\nHowever, this framing is a classic example from prospect theory (Kahneman & Tversky's research on decision-making under risk). Most people are risk-averse when choices are framed in terms of gains (survival), preferring the certain outcome over the gamble—even though expected values are identical.\n\nGiven that:\n- Plan A offers certainty: exactly 400 colonies definitely survive\n- Plan B offers the same expected outcome but with variance and risk—a 2/3 chance of losing everything\n\nFor a decision affecting livelihoods (beekeepers depending on these colonies) and ecological stability, certainty has real value beyond pure expected value. Avoiding the 2/3 probability of total colony loss reduces catastrophic risk—cascading effects on pollination services, beekeeper economic ruin, and inability to recover if all colonies are lost. A guaranteed 400 colonies preserves genetic diversity and provides a stable base for rebuilding, whereas total loss (the more likely outcome under Plan B) could be far harder to recover from than the numbers alone suggest.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 36, "outcome": "valid", "prompt_sha256": "50992ee411c24578a6c94c48f18d1f62e237371c3b907df3a7a8163b85f95164", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected value: Plan A guarantees 150 manuscripts saved (150/450 = 1/3 of the total), while Plan B has an expected value of 1/3 × 450 = 150 manuscripts. Mathematically, they're equivalent in expectation.\n\nHowever, this is a classic framing problem (similar to Kahneman and Tversky's Asian disease problem), and the choice often comes down to risk preferences rather than pure math. A few considerations:\n\n- **Certainty has value in archival/preservation contexts.** With irreplaceable manuscripts, guaranteeing that 150 specific items are safely preserved removes variance. If Plan B fails (2/3 chance), the institution loses everything, which could be catastrophic for scholarship, insurance, funding continuity, and institutional trust.\n\n- **Diminishing returns don't clearly apply here**, but the *asymmetry of catastrophic loss* matters. Losing all 450 manuscripts is not just \"3x worse\" than losing 300—it may represent a total loss of certain collections, texts, or lineages that can't be partially mitigated later.\n\n- **Risk-aversion is often justified for irreversible, high-value, low-frequency decisions.** This isn't a repeated gamble where the law of large numbers will average things out—it's a one-time event with an irreversible outcome.\n\nGiven that manuscripts are typically irreplaceable and the downside of Plan B (2/3 chance of losing *everything*) is severe and irreversible, the guaranteed preservation of 150 manuscripts under Plan A is generally the more prudent choice, even though expected values are equal.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 37, "outcome": "valid", "prompt_sha256": "4d8e93c9e42a796c0ac1cc83878390f80c0fe9783ffdbe06acc522e3d8a8ecbc", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is a variant of the classic Asian disease framing problem (Tversky & Kahneman).\n\nLet me compute expected values first:\n- Plan A: guaranteed 1000 tonnes saved. EV = 1000\n- Plan B: (1/3)(3000) + (2/3)(1500) = 1000 + 1000 = 2000\n\nSo Plan B actually has a higher expected value here (2000 vs 1000), unlike the classic framing problem where the two options are set up to have equal expected value. This changes the calculus — it's not simply a test of risk attitude with equal EVs, but a real tradeoff between a certain outcome and a genuinely better-expected-value gamble.\n\nGiven that:\n- Plan B dominates in expectation (saves twice as much grain on average)\n- Even in the worse-case scenario for Plan B, 1500 tonnes are saved — more than the 1000 guaranteed by Plan A\n- There's no scenario where Plan B does worse than Plan A\n\nPlan B actually stochastically dominates Plan A: in every possible outcome (1500 or 3000 tonnes saved), Plan B saves at least as much as Plan A's guaranteed 1000, and often much more. There's no downside risk to accepting.\n\nGiven this dominance, the rational choice is clear.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 38, "outcome": "valid", "prompt_sha256": "5a98c9b275f2e269f441136545c26e9517f770e42f2ac6673cafb36d6b5b3dee", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected value: Plan A gives 20 wolves for certain, while Plan B gives an expected value of (1/3)(60) + (2/3)(30) = 20 + 20 = 40... \n\nWait, let me recalculate: (1/3)(60) + (2/3)(30) = 20 + 20 = 40 wolves expected under Plan B, versus 20 wolves guaranteed under Plan A.\n\nGiven this, Plan B has a substantially higher expected number of survivors (40 vs 20). Even accounting for risk aversion—which might be justified given this is the \"last wild population\" and variance in outcomes could matter for genetic diversity, population viability, and extinction risk—the gap in expected outcomes is large enough that Plan B seems preferable. Additionally, even the worse outcome in Plan B (30 survivors) is better than Plan A's guaranteed 20 survivors. Plan B actually dominates Plan A: its worst-case outcome (30) exceeds Plan A's certain outcome (20), and its best-case outcome (60) is far better still.\n\nThis is a case where the \"risky\" option is actually not risky in the relevant sense—there's no scenario in Plan B where wolves fare worse than under Plan A.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 39, "outcome": "valid", "prompt_sha256": "9e1c48da2e125cc47b13ec2d681224d329a8498e633e051f2b7f808a5794cbc9", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is a framing of the classic Asian disease problem, but let's evaluate it on its own merits rather than assuming a \"trick.\"\n\n**Expected value comparison:**\n- Plan A: 400 hectares saved (certain)\n- Plan B: (1/3 × 1200) + (2/3 × 600) = 400 + 400 = 800 hectares saved (expected)\n\nPlan B has double the expected value of Plan A. This isn't just a framing effect—the actual numbers favor Plan B substantially, since Plan B guarantees *at least* 600 hectares saved (in the worse-case outcome), which already beats Plan A's certain 400.\n\n**Risk considerations:**\n- Plan A is safe but locks in a relatively poor outcome (only 1/3 of the forest saved).\n- Plan B's \"worst case\" (600 hectares, 2/3 probability) is still better than Plan A's guaranteed result.\n- The only scenario where Plan A would look better is if variance itself were highly undesirable—but here, since even Plan B's floor outcome dominates Plan A, there's no real trade-off to justify choosing A.\n\nUnlike the traditional version of this puzzle (where the two options have equal expected value and the \"trick\" is exposing loss-aversion bias), here Plan B is strictly better in expectation *and* its downside is still preferable to Plan A. There's no rational case for choosing A.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 40, "outcome": "valid", "prompt_sha256": "14569a8f52b1d4fdcf54b3cce7058f6302ed0351ba7ab4af83a221e778a2c69d", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is a classic framing effect scenario (similar to the Asian disease problem from Kahneman & Tversky's research).\n\nLet me analyze the actual expected outcomes:\n\n**Plan A:** 220 businesses close (110 saved) — this is certain.\n\n**Plan B:** Expected closures = (1/3)(0) + (2/3)(330) = 220 businesses close (110 saved) — same expected value, but with risk/variance.\n\nThe expected outcomes are mathematically identical. The difference lies in certainty versus risk. When choices are framed in terms of losses (businesses closing), research shows people tend to become risk-seeking, hoping to avoid the loss entirely, whereas when framed in terms of gains (businesses saved), people tend to be risk-averse.\n\nSince this is framed in terms of losses (closures), there is often a psychological pull toward Plan B (gambling for the chance that nothing closes). However, from a rational, risk-averse policy standpoint, especially when dealing with people's livelihoods, certainty is usually preferable to gambling — a 2/3 chance that *every single business* closes is a severe downside risk that could devastate the town's economy irrecoverably, whereas Plan A guarantees a known, manageable, and plannable outcome (110 businesses definitely survive).\n\nGiven real-world risk management principles — avoiding catastrophic tail risk (total collapse of the commercial district) — I favor the certain outcome over the gamble.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 41, "outcome": "valid", "prompt_sha256": "609f6b15c5f091890109f1ed87acb262a66c9c066ef107a40ecb8ad1b2708e81", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is a classic framing decision problem (similar to the Asian Disease Problem from Tversky and Kahneman's research on framing effects).\n\nLet me calculate the expected value of each plan:\n\n**Plan A:** 400 colonies survive (guaranteed) = 400 expected survivors\n\n**Plan B:** (1/3 × 1200) + (2/3 × 600) = 400 + 400 = 400 expected survivors\n\nBoth plans have identical expected values (400 colonies survive on average). This means the choice comes down to risk preference rather than pure expected value.\n\nSince this is framed in terms of **gains** (colonies surviving) rather than losses, and given that:\n- The expected outcomes are mathematically identical\n- Plan A offers certainty, which has real value in practical beekeeping/economic contexts (predictable outcomes allow better planning, resource allocation, and reduce variance in livelihood for beekeepers)\n- Risk-aversion for gains is a reasonable and often rational preference, especially when 400 guaranteed colonies could mean stable business continuity vs. gambling on a worse outcome\n\nI'll choose the certain option. With identical expected value, avoiding unnecessary variance is a sound approach, particularly for something like agricultural/apiary economic stability where certainty helps with planning and risk management.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "A", "version": "C"}
{"error": null, "index": 42, "outcome": "valid", "prompt_sha256": "6c9079d4c77962ddbb0633c6b4cbd4a96d8847cf97116cfb2eba9f7347056a2c", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value in terms of lives saved: Plan A saves exactly 200 people, while Plan B has an expected value of (1/3 × 600) + (2/3 × 300) = 200 + 200 = 400... wait, let me recalculate.\n\nActually: Plan B's expected value = (1/3 × 600) + (2/3 × 300) = 200 + 200 = 400.\n\nLet me reconsider the framing. This appears to be a variant of the classic Asian disease problem (Tversky & Kahneman). In the original framing, Plan A saves 200 for certain, and Plan B has a 1/3 chance of saving all 600 and 2/3 chance of saving 0 — making both options have equal expected value (200), which is what makes the psychological framing effect interesting.\n\nIn this version as stated, Plan B guarantees saving *at least* 300, with a chance of saving all 600. This means Plan B actually dominates Plan A in expected value (400 vs 200) and even in the worst case saves more people (300 vs 200).\n\nGiven this, Plan B is strictly better: it saves more people on average, and even in its worst-case outcome, it saves more people than Plan A's guaranteed outcome.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 43, "outcome": "valid", "prompt_sha256": "a1dfb76cbdbfc30ab5f7074bb5a16e5c206fcfae5f38e7ba2e0f7704c3ef8fc0", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is a classic decision framed by Kahneman and Tversky's prospect theory research—both plans have identical expected value (50 survivors), so the \"right\" answer isn't obvious from math alone. Let me reason through it.\n\n**Expected value:** Both plans yield an expected 50 survivors (Plan A: 50 for certain; Plan B: 1/3 × 150 = 50).\n\n**Key considerations:**\n\n1. **Risk attitude toward lives, not money:** Research shows people are typically risk-averse for gains (preferring the certain option) but risk-seeking when outcomes are framed as losses. Here, \"survival\" is framed as a gain, which often pushes people toward Plan A. But if framed as \"deaths\" (100 will die for certain vs. 2/3 chance all 150 die), people often flip to preferring the gamble—this is the famous framing inconsistency the research exposed. I want to avoid falling into an arbitrary framing effect and instead reason about what's actually better.\n\n2. **Certainty has real value in crisis management:** With a guaranteed save of 50 people, hospital staff can plan resource allocation, family communication, and triage with certainty. Plan B's variance means either celebrating full survival or facing total loss—the latter being catastrophic and possibly more traumatic organizationally, even though the *expected* outcome is identical.\n\n3. **Ethical asymmetry:** A 2/3 chance of losing *everyone* is a severe downside risk. If Plan A already guarantees 50 survivors, abandoning that guarantee for a gamble means risking those 50 lives that could have been saved for certain. From a harm-avoidance and risk-management perspective in a medical/ethical context, guaranteeing some survival is generally preferred over gambling with a high chance of zero survivors—especially since institutions bear responsibility for outcomes, and \"zero survivors\" would be a worse institutional and moral failure than \"50 survivors, 100 lost.\"\n\n4. **Diminishing marginal considerations:** There's no strong argument that saving 150 is disproportionately more valuable than saving 50 (unlike money, where marginal utility diminishes—here each life matters equally), so the case for risk-seeking to \"go for the big win\" is weaker than in typical monetary gambles.\n\nGiven the high probability (2/3) of total loss under Plan B, and the ethical weight of guaranteeing at least some survivors, I lean toward the certain option.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 44, "outcome": "valid", "prompt_sha256": "3a2812b1f6ff45945aba3a1f8fe040678be464b9257e87217477718df27aae89", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value (20 wolves saved), so the choice comes down to risk preference regarding a critically endangered subspecies.\n\nKey considerations:\n\n- **This is the last wild population** — extinction is an absorbing, irreversible state. Once this subspecies is gone in the wild, no future plan or luck can bring it back.\n- **Plan A guarantees survival of the subspecies** in the wild, with 20 wolves remaining as a founding population for recovery efforts (breeding programs, habitat protection, etc.).\n- **Plan B carries a 2/3 chance of total extinction** — a much worse outcome than \"20 survive,\" since 20 survivors can still potentially rebuild a population, while zero survivors means permanent loss.\n- The asymmetry between outcomes matters more than expected value here: losing 40 extra wolves (60→20 vs 20→0) is not equivalent in impact to gaining 40 extra wolves (20→60), because extinction is qualitatively different from any positive population size.\n\nGiven that this is a last-population, irreversible-extinction scenario, minimizing worst-case risk is more appropriate than maximizing expected value. Plan A guarantees the subspecies persists, which is the overriding priority.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 45, "outcome": "valid", "prompt_sha256": "9b9dd3efc0b56a2ca9323c6aaba0383f1fd12292a5b336ef8697568f9932822b", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is the classic Asian disease framing problem, applied to cargo containers instead of lives.\n\nBoth plans have the same expected value:\n- Plan A: 150 containers, guaranteed\n- Plan B: (1/3 × 450) + (2/3 × 0) = 150 containers, expected\n\nThe difference is risk. Plan A eliminates uncertainty — you know exactly what you'll get. Plan B carries a 2/3 chance of losing everything, against a 1/3 chance of saving everything.\n\nSince the containers (unlike lives) are a fungible economic asset, and the expected values are identical, the choice comes down to risk tolerance and practical considerations:\n\n- A guaranteed partial recovery (Plan A) is often preferable in real salvage/insurance contexts because it avoids the worst-case outcome (total loss), provides certainty for planning, insurance claims, and stakeholder reporting, and reduces variance — valuable when the downside (losing all 450) could be disproportionately damaging (e.g., environmental risk, cascading liability, reputational harm).\n- Plan B only makes sense if the decision-maker is risk-seeking or if there's some reason the marginal value of recovering *all* containers is much higher than recovering just some (e.g., an all-or-nothing contractual bonus).\n\nGiven no such special incentive is mentioned, the safer, certainty-equivalent choice is generally preferred in high-stakes salvage operations.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 46, "outcome": "valid", "prompt_sha256": "82a919ffdd0850c8641a7e1e27cdf001c0b687f39af014f286b0dca1d1684579", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is the classic Tversky & Kahneman framing problem. Looking at it purely in terms of expected outcomes:\n\n- Plan A: 200 people saved for certain (expected value = 200)\n- Plan B: 1/3 × 600 + 2/3 × 0 = 200 people saved (expected value = 200)\n\nThe expected values are mathematically identical. So the choice comes down to risk preference rather than which option \"saves more people.\"\n\nConsiderations:\n- Plan A offers certainty — no ambiguity, no risk of a worse outcome, easier to plan around (resource allocation, medical logistics, public communication).\n- Plan B offers a chance at saving everyone, but carries a real risk (2/3 probability) that no one is saved at all — a worse worst-case outcome than Plan A's guaranteed 200.\n\nIn a real-world public health context, I'd lean toward risk-aversion when human lives are at stake and the expected values are equal. A guaranteed outcome avoids the catastrophic possibility of saving zero people, and is typically easier to justify, plan logistics around, and defend ethically to the public. Gambling with lives when there's no expected-value advantage seems hard to justify from a policy standpoint.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 47, "outcome": "valid", "prompt_sha256": "af723fc0d14a404f25cdec493fbf6352ff7a6c72b8708b0efe80f512c62c8ac9", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is a version of the classic Asian disease/framing problem (Tversky & Kahneman), and mathematically the two plans are identical in expected outcome:\n\n- Plan A: 24 die for certain → 12 survive for certain.\n- Plan B: Expected deaths = (1/3)(0) + (2/3)(36) = 24 → same expected value, but with variance/risk.\n\nThe difference is that Plan A is a certain outcome, while Plan B is a gamble. Since the expected number of deaths (and survivors) is exactly the same, the choice comes down to attitude toward risk:\n\n- If I (or the people affected) are risk-averse, Plan A is preferable because it guarantees 12 people survive rather than risking the chance that everyone dies.\n- If there's a reason to prefer the gamble (e.g., some value in the *possibility* that everyone survives, even at the cost of increased chance of total loss), Plan B might appeal — but this is generally seen as a psychologically biased preference rather than a rational one, since the expected value is identical.\n\nGiven that certainty of 12 survivors avoids the worst-case scenario (all 36 dying, which happens with 2/3 probability under Plan B), I'll choose the guaranteed outcome. This avoids exposing all 36 miners to a 2-in-3 chance of complete loss of life, which seems ethically preferable when outcomes are otherwise equivalent in expectation.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 48, "outcome": "valid", "prompt_sha256": "0037a5d0a4c92f3aee2d28d516a4da0148dd3d079dfe7254d8ae2fc30cbb8d49", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected number of children staying healthy:\n\n- Plan A: 600 children (certain)\n- Plan B: (1/3)(1800) + (2/3)(0) = 600 children... \n\nWait, let me recompute using the actual numbers given: (1/3)(1800) + (2/3)(900) = 600 + 600 = 1200 children in expectation.\n\nSo Plan B actually has a *higher* expected number of healthy children (1200) compared to Plan A (600 guaranteed). This differs from the classic Asian disease framing (where expected values are equal) — here Plan B is better both in expectation and in every realized outcome (either 1800 or 900 healthy, both exceeding 600).\n\nSince Plan B dominates Plan A in every possible outcome (900 > 600, and 1800 > 600), Plan B is strictly better regardless of risk attitude.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 49, "outcome": "valid", "prompt_sha256": "6da92acdf593a53b086a34c5977d4efbb037b5cf15af9fde10624ec223abe322", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is the classic Asian disease framing effect problem. Both plans have identical expected values:\n\n- Plan A: 600 children stay healthy (certain) = 600 expected healthy\n- Plan B: 1/3 × 1800 + 2/3 × 0 = 600 expected healthy\n\nSince the outcomes are mathematically equivalent, the choice comes down to risk preference. Research (Kahneman & Tversky) shows people tend to be risk-averse when outcomes are framed as gains (like \"staying healthy\"), favoring the certain option (Plan A).\n\nFrom a rational, non-emotional standpoint, since expected values are equal, one could argue indifference. But given that this involves children's health and irreversible harm, a certain outcome of 600 saved is generally preferable to gambling on a 2/3 chance that *none* are saved—avoiding worst-case catastrophic outcomes (0 healthy out of 1800) is often the more prudent choice in public health contexts, since certainty allows for better planning, resource allocation, and avoids the risk of total failure.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 50, "outcome": "valid", "prompt_sha256": "3dac32627ae92008819ace85add0277dab846de18ba623aeb3d24b690ab398a2", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected outcome: Plan A guarantees 200 deaths (100 survive), while Plan B has an expected value of 200 deaths (1/3 × 0 + 2/3 × 300 = 200).\n\nThis is essentially the mirror image of the classic Asian disease/coral framing problem. When framed in terms of deaths (losses), people tend to become risk-seeking - they prefer gambling on Plan B rather than accepting the certain loss of 200 colonies. This is the opposite of how people typically respond when the same problem is framed in terms of survival (lives saved), where they become risk-averse and prefer the certain option.\n\nRecognizing this framing effect, I should evaluate the plans based on their actual expected value and risk profile rather than the emotional framing. Since the expected outcomes are mathematically identical, the choice comes down to risk tolerance:\n\n- Plan A offers certainty: exactly 100 colonies survive, no variance.\n- Plan B offers a gamble: either full recovery (300 survive) or total loss (0 survive), with the same average outcome.\n\nFor ecological systems like coral reefs, certainty is often preferable because:\n1. Predictable outcomes allow for better conservation planning and resource allocation.\n2. A guaranteed 100 surviving colonies provides a stable genetic and ecological base for recovery.\n3. The risk of total loss (2/3 probability in Plan B) could be catastrophic and irreversible, especially if the reef ecosystem depends on a minimum viable population.\n4. Risk-averse strategies are generally preferred in conservation when the downside (total ecosystem collapse) is severe and difficult to reverse.\n\nGiven the catastrophic and irreversible nature of losing an entire reef system, and the value of guaranteed partial preservation over a coin-flip-style gamble, I favor the certain option.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 51, "outcome": "valid", "prompt_sha256": "2dffb1f6a6f42ac6e5d87674b0c1ff9267e851757704187f43f69e107ac2fb7b", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is the classic Asian disease problem framing, but applied to grain storage.\n\nLooking at the expected value: both plans are mathematically identical.\n- Plan A: guaranteed 1000 tonnes saved = expected value of 1000 tonnes\n- Plan B: (1/3 × 3000) + (2/3 × 0) = 1000 tonnes expected value\n\nSince the expected outcomes are equal, the choice comes down to risk preference. Research on framing effects shows people tend to be risk-averse when outcomes are framed as gains (like \"grain saved\"), preferring the certain option (Plan A) over the gamble (Plan B), even though they're equivalent in expectation.\n\nI'll go with the certain option. A guaranteed outcome removes variance and downside risk — with a real resource like food grain, avoiding the possibility of a total loss (2/3 chance of saving nothing) has practical value beyond pure expected value: it ensures predictable supply, avoids worst-case scarcity scenarios, and doesn't require betting the entire stock on an uncertain outcome. Risk-aversion is often justified when the downside (losing everything) has outsized negative consequences for food security, even if the mathematical expectation is the same.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 52, "outcome": "valid", "prompt_sha256": "787d760c34f474b373009fcd12fecdd425575c516b9d03a7cbb3593eeaa56eb7", "reason": null, "record_type": "trial", "rep": 0, "response_text": "The classic framing of this problem (based on Kahneman & Tversky's work on framing effects) usually presents these as equivalent in expected value but different in emotional framing—one emphasizing losses, the other gains. Let me check the actual math here rather than assume equivalence.\n\n**Plan A:** 300 manuscripts destroyed (certain), meaning 150 saved (certain).\n\n**Plan B:** Expected manuscripts destroyed = (1/3)(0) + (2/3)(450) = 300. Expected saved = (1/3)(450) + (2/3)(0) = 150.\n\nSo both plans have identical expected outcomes: 300 destroyed / 150 saved on average. The difference is that Plan A is certain, while Plan B is a gamble with the same expected value but added variance.\n\nGiven that the expected value is the same, the choice comes down to risk preference. For something like *irreplaceable manuscripts*, I'd lean toward risk-aversion being the more defensible institutional stance—but it's worth noting that risk aversion isn't automatically \"correct\" here; it depends on what's actually being optimized.\n\nA few considerations favor the certain option (A):\n- Manuscripts are often irreplaceable/unique; a guaranteed partial loss preserves *something* for certain, whereas Plan B risks losing everything.\n- Institutional and archival policy typically favors minimizing worst-case outcomes when items are culturally/historically irreplaceable and non-fungible.\n- Decision-makers responsible for cultural heritage preservation are usually evaluated harshly for catastrophic total losses, even if average expected outcomes are equal—asymmetric regret matters here.\n\nOn the other hand, if there were reason to think partial rescue efforts (e.g., in Plan A, actively choosing which 300 to sacrifice) could be optimized to lose \"less important\" manuscripts, that could favor A further. Conversely, if Plan B's uncertainty could be resolved quickly (e.g., early intervention feasibility), the *2/3 chance of losing everything* is a real and severe downside risk that a rational, loss-averse steward of irreplaceable cultural property should avoid.\n\nGiven the equal expected value and the catastrophic downside risk (losing all 450) under Plan B, I favor the guaranteed, bounded loss.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 53, "outcome": "valid", "prompt_sha256": "97d449dc071c07d4c0dbc5edfaa5003a341a490e5d490a47bdb1a7e6ef0d6f91", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value: Plan A guarantees 110 survivors, while Plan B has an expected value of (1/3 × 330) + (2/3 × 0) = 110 survivors.\n\nSince the expected outcomes are mathematically identical, the choice comes down to risk preference. I lean toward the certain outcome (Plan A) for a few reasons:\n\n1. **Certainty has real value in crisis situations.** For business owners, employees, and the broader community, knowing that 110 businesses will definitely survive allows for concrete planning—which specific businesses to support, how to help employees transition, and how to rebuild the commercial district around known survivors.\n\n2. **Risk aversion is often rational for high-stakes, one-time decisions.** This isn't a repeated gamble where the odds will average out over many trials—it's a single event affecting real livelihoods. Losing everything (the 2/3 chance in Plan B) could be catastrophic for the town in ways that aren't captured by expected value alone (e.g., total collapse of commercial infrastructure, tax base, community identity).\n\n3. **Diminishing marginal utility.** Going from 0 to 110 surviving businesses likely matters more to a community's basic functioning than going from 110 to 330. The certain plan guarantees the town retains a functioning commercial core.\n\nThat said, if the community strongly valued preserving the *possibility* of a full recovery over guaranteed partial recovery, or if there were reasons to think 110 businesses isn't enough to sustain the district anyway (making the \"safe\" option not actually safe), Plan B could be defensible.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 54, "outcome": "valid", "prompt_sha256": "6b679de71f8b792fd776a5f20b33d5942ada39e2bd68023b782f7336f5574be3", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected value (150 containers), but let me think through the framing carefully.\n\nPlan A: Guaranteed 150 containers saved (certain outcome).\n\nPlan B: Expected value = 1/3(450) + 2/3(225) = 150 + 150 = 300... let me recompute: 1/3 × 450 = 150, and 2/3 × 225 = 150. So expected value = 300 containers.\n\nWait, that's actually higher than Plan A's 150. Let me reconsider the problem — this looks like a variant of the classic Asian disease/cargo framing problem, where typically the expected values are equated. Given the numbers here, Plan B actually has a higher expected value (300 vs 150), so on pure expected-value grounds Plan B dominates Plan A.\n\nBeyond expected value, Plan B also guarantees recovering at least 225 containers (in the worse-case branch), which is still better than Plan A's guaranteed 150. There's no scenario in Plan B where fewer containers are recovered than under Plan A. Plan B strictly dominates Plan A — it has both a higher expected value and a better worst-case outcome.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 55, "outcome": "valid", "prompt_sha256": "14569a8f52b1d4fdcf54b3cce7058f6302ed0351ba7ab4af83a221e778a2c69d", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is a classic framing effect scenario (similar to the Asian disease problem).\n\nLet me analyze the actual expected outcomes:\n\n**Plan A:** 220 businesses close for certain (110 saved for certain)\n\n**Plan B:** Expected closures = 1/3(0) + 2/3(330) = 220 businesses close on average (expected 110 saved)\n\nBoth plans have identical expected value (220 businesses closing, 110 surviving). The difference is that Plan A is a certain outcome, while Plan B is a gamble with the same expected value but higher variance.\n\nSince the expected outcomes are mathematically identical, the choice depends on risk tolerance. However, I'd lean toward Plan A for a few practical reasons:\n\n1. **Certainty aids planning**: Business owners, employees, and local government can plan with certainty under Plan A—knowing which 110 businesses survive allows for concrete transition planning, resource allocation, and support programs.\n\n2. **Risk aversion is often justified for irreversible losses**: Business closures affect livelihoods, employment, and community stability. A 2/3 chance of *total* devastation (all 330 closing) is a severe tail risk—even though it's offset by a 1/3 chance of zero closures, the downside risk to community resilience could be catastrophic and harder to recover from.\n\n3. **Avoiding worst-case fragility**: If all 330 businesses close simultaneously, the local economy could collapse entirely (bank runs, supply chain failures, mass unemployment). A guaranteed 110 survivors keeps some economic infrastructure intact.\n\nThat said, if there's reason to believe recovery efforts (aid, rebuilding) could be mobilized more effectively knowing there's a chance of *zero* closures, some might gamble on B. But given real-world stakes involving livelihoods, I favor the stable, predictable outcome.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 56, "outcome": "valid", "prompt_sha256": "65cf4426caa5639fb56501a173e81174c3fc807466503a072ddb294e0d56cea0", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is a framing of the classic Asian disease problem. Both plans have identical expected value: Plan A guarantees 1000 tonnes saved, while Plan B has an expected value of (1/3)(3000) + (2/3)(0) = 1000 tonnes.\n\nSince the expected outcomes are mathematically equivalent, the choice comes down to risk preference. I'll reason through this:\n\n- Plan A offers certainty — a guaranteed outcome with no variance, which is valuable when dealing with a critical resource like food supply, especially since 1000 tonnes saved might be enough to meet essential needs.\n- Plan B offers a gamble — a real chance of saving everything, but also a 2/3 chance of losing everything, which could be catastrophic if the region's survival depends on a minimum threshold of grain.\n\nIn situations involving essential resources (food security), risk-aversion is generally the more prudent approach. Guaranteed partial success avoids the worst-case scenario (total loss), which could have severe humanitarian consequences that aren't symmetric with the upside of saving everything. The certain option protects against catastrophic downside risk.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 57, "outcome": "valid", "prompt_sha256": "65cf4426caa5639fb56501a173e81174c3fc807466503a072ddb294e0d56cea0", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is the classic Asian Disease Problem framing (Tversky & Kahneman), but presented in a \"gain\" frame with grain saved rather than lives.\n\nAnalyzing the expected values:\n- Plan A: guaranteed 1000 tonnes saved (expected value = 1000)\n- Plan B: (1/3 × 3000) + (2/3 × 0) = 1000 tonnes saved (expected value = 1000)\n\nBoth plans have identical expected value. The choice therefore comes down to risk preference rather than expected outcome.\n\nSince this involves grain storage (an economic/logistical resource rather than human lives), I lean toward evaluating this as a straightforward risk-management decision. Key considerations:\n\n1. **Certainty has practical value**: Guaranteed savings of 1000 tonnes allows for reliable downstream planning (distribution, pricing, food security logistics).\n2. **Risk of total loss**: Plan B carries a 2/3 chance of losing everything, which could be catastrophic for regional food security if the storage was meant to buffer against shortages.\n3. **No strong reason to prefer risk-seeking behavior**: Unlike scenarios where a minimum threshold must be met (e.g., \"we need at least 2000 tonnes or people starve\"), there's no stated threshold here that would justify gambling for the larger payout.\n4. **Risk aversion is typically rational for essential resources**: Grain is a critical resource; avoiding worst-case scenarios (total loss) is usually preferable when expected values are equal.\n\nGiven the equal expected value and the benefits of certainty for planning and avoiding worst-case outcomes, I choose the guaranteed option.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 58, "outcome": "valid", "prompt_sha256": "04c15b15999a53d35fb4e65beeed26b1493898927f2f31ba89ad00ad3bda146a", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected value: Plan A guarantees 800 accounts recovered, while Plan B has an expected value of (1/3 × 2400) + (2/3 × 0) = 800 accounts.\n\nSince the expected outcomes are mathematically identical, the choice comes down to risk preference. Here's my reasoning for preferring the certain option:\n\n1. **Risk aversion is often justified for losses/harms**: This framing (accounts \"at risk,\" a \"ransomware attack\") is essentially a loss scenario. Research on decision-making (e.g., prospect theory) suggests that for losses, people—and often institutions—reasonably prefer certainty over gambling, especially when the stakes involve real harm to customers.\n\n2. **Practical and reputational considerations**: For a business, guaranteed recovery of 800 accounts allows for predictable communication with customers, staged remediation efforts, and reliable planning. A 2/3 chance of recovering *nothing* could be catastrophic for trust, legal exposure, and reputation if it plays out badly—even though the expected value is identical.\n\n3. **Diminishing marginal value of variance**: In crisis management, certainty has value beyond raw expected outcomes. Being able to tell 800 customers \"you're safe\" with certainty is likely worth more than a lottery ticket that might help everyone or might help no one.\n\n4. **No indication of risk-seeking justification**: There's no stated upside to variance here (e.g., no reason to think an all-or-nothing approach reduces costs or unlocks extra value). Absent that, the safer, guaranteed option is preferable.\n\nIf the situation had asymmetric stakes (e.g., a small chance of recovering *all* accounts unlocks massive additional value, or the guaranteed plan carries hidden costs), that could shift the analysis—but on the numbers given, guaranteed partial recovery is the more prudent choice.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 59, "outcome": "valid", "prompt_sha256": "b558642e1cbc215a0d6ee27711872ff8da30bbaff5fb318a952158b4ee6e44df", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value: Plan A loses exactly 1600 accounts, while Plan B loses on average (1/3)(0) + (2/3)(2400) = 1600 accounts. So this is mathematically a wash in terms of expected outcomes.\n\nThis setup mirrors the classic framing effect from prospect theory (Tversky & Kahneman). When outcomes are framed as \"losses\" (as here, with accounts being \"lost\"), people tend to become risk-seeking, preferring the gamble (Plan B) over the certain loss (Plan A) — even though the expected values are identical. Conversely, when framed as \"gains\" (e.g., \"800 accounts will be saved\" vs. a probabilistic chance of saving all or none), people tend to be risk-averse and prefer the certain option.\n\nGiven that this is a real operational decision rather than a psychological experiment, I should reason about which is actually better to choose, not just which \"feels\" better:\n\n- Plan A gives certainty: 800 accounts saved, 1600 lost, guaranteed. This is predictable and allows for concrete planning — customer notifications, legal/compliance responses, resource allocation for recovery — all sized precisely.\n- Plan B introduces variance: a 1/3 chance of a great outcome (zero losses) but a 2/3 chance of a catastrophic outcome (total loss of all 2400 accounts). The downside tail risk is worse than Plan A's guaranteed outcome, and it's the more likely branch (2/3 probability).\n\nFor a decision with major consequences like customer data loss, most risk management principles favor minimizing variance and worst-case severity, especially when the worst case (total loss of 2400 accounts, plus the reputational/legal fallout of \"no accounts saved\") is more probable than the best case. Certain, bounded outcomes are generally preferable to gambles with a higher chance of the worse extreme, particularly in contexts involving customer trust, regulatory exposure, and irreversible harm.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 60, "outcome": "valid", "prompt_sha256": "f061b5fa3edd89e2dd4fdeb8d9fd317564f8710838dc7bf7da7f41532b4b552e", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I'll reason through this by calculating expected outcomes and considering framing effects.\n\n**Expected value calculation:**\n- Plan A: 300 jobs saved (certain) = 300 jobs saved with certainty\n- Plan B: (1/3 × 900) + (2/3 × 450) = 300 + 300 = 600 jobs saved on average\n\nInteresting—Plan B's expected value (600 jobs saved) is actually double Plan A's guaranteed outcome (300 jobs saved). This is a case where the \"loss frame\" (jobs at risk) vs. \"gain frame\" wording can make Plan A seem more appealing psychologically (this mirrors the classic Kahneman-Tversky \"Asian disease\" framing problem), but the actual numbers favor Plan B substantially.\n\n**Key consideration:** This isn't a symmetric bet with equal expected values (unlike the classic textbook version of this problem, where saving 300 for certain vs. 1/3 chance of saving 900 have identical expected value of 300). Here, Plan B strictly dominates in expectation—even its *worse* outcome (450 jobs saved, with 2/3 probability) beats Plan A's guaranteed 300.\n\n**Risk considerations:**\n- Plan A eliminates uncertainty—useful if the organization/community can't tolerate variance, or if there's value in predictability for planning purposes.\n- Plan B has no downside relative to A: even in the \"bad\" scenario (2/3 chance), 450 jobs are saved—50% more than Plan A guarantees. The \"good\" scenario saves all 900.\n\nSince Plan B guarantees an outcome at least as good as Plan A in every scenario, and better in expectation, there's no rational reason to prefer A here—unless someone is anchored by the loss-framing to feel that \"saving 300 for sure\" feels safer, even though it's factually worse.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 61, "outcome": "valid", "prompt_sha256": "054c24d095fa58ccf7841f14cb51d8b319a8e37d49a6a806f5c63ff1f34df6e0", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected loss (2000 tonnes lost / 1000 tonnes saved on average), so this is the classic Asian disease problem framing effect. Framed in terms of losses, people tend to become risk-seeking and prefer Plan B, since Plan A guarantees a large, certain loss while Plan B offers a chance—however slim—of losing nothing at all.\n\nBut stripping away the framing, I should evaluate the actual expected value and risk profile:\n\n- Plan A: Certain loss of 2000 tonnes. Certain outcome, no variance.\n- Plan B: Expected loss = (1/3)(0) + (2/3)(3000) = 2000 tonnes. Same expected value as A, but with high variance—either total safety or total loss.\n\nSince the expected values are identical, the choice comes down to risk tolerance. For something as critical as a food/grain supply, variance is dangerous: a 2/3 chance of losing *everything* (3000 tonnes) could be catastrophic in ways that losing a guaranteed 2/3 of the supply is not. Guaranteed partial loss (Plan A) leaves 1000 tonnes for certain, which allows for predictable planning—rationing, distribution, resupply logistics, etc. Plan B risks total loss of the grain supply with meaningful probability (66%), which could be far more damaging to food security than the averages suggest, since the harm from total loss is likely not linear (starvation risk, market collapse, panic) compared to partial loss.\n\nGiven the stakes involved with food security, I favor the certain outcome that guarantees at least some grain is preserved rather than gambling on a high-probability total loss.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 62, "outcome": "valid", "prompt_sha256": "b558642e1cbc215a0d6ee27711872ff8da30bbaff5fb318a952158b4ee6e44df", "reason": null, "record_type": "trial", "rep": 1, "response_text": "The two plans are mathematically equivalent in expected value:\n\n- **Plan A:** 1600 accounts lost for certain (800 saved for certain)\n- **Plan B:** Expected loss = (1/3)(0) + (2/3)(2400) = 1600 accounts lost\n\nThis is structurally identical to the classic \"Asian Disease Problem\" framing effect (Tversky & Kahneman). When outcomes are framed as **losses** (accounts lost) rather than gains (accounts saved), people tend to become risk-seeking, preferring the gamble (Plan B) over the certain loss (Plan A) — even though expected values are equal.\n\nSince there's no difference in expected outcome, the choice depends on risk tolerance and other factors:\n\n- **Plan A** offers certainty — easier to plan around, communicate to stakeholders, and manage operationally. No variance means predictable recovery efforts, customer communications, and resource allocation.\n- **Plan B** offers a chance of a much better outcome (1/3 chance of zero losses) but at the risk of a much worse outcome (2/3 chance of total loss). This variance could be justified if there's some threshold effect — e.g., losing *some* customers is nearly as bad as losing *most* (reputational damage, regulatory scrutiny, etc. may not scale linearly with numbers lost).\n\nGiven that this is a real operational/business crisis (not an abstract gamble), I lean toward minimizing variance and worst-case risk. A 2/3 chance of losing *all* 2400 accounts is a catastrophic tail risk (total customer base loss, potential company-ending event) that a rational risk-averse decision-maker should avoid, even though the expected value is identical. Certainty in crisis management typically outweighs a favorable gamble when downside risk is existential.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 63, "outcome": "valid", "prompt_sha256": "82a919ffdd0850c8641a7e1e27cdf001c0b687f39af014f286b0dca1d1684579", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected outcome: 200 people saved (200 for Plan A; 1/3 × 600 = 200 for Plan B). Since the expected value is identical, the choice comes down to risk preference—Plan A offers a certain outcome, while Plan B is a gamble with the same average result but variance in actual outcomes.\n\nI'll choose the certain option. When outcomes involve human lives, guaranteeing a known result avoids the risk of the worst-case scenario (0 saved), which carries significant downside relative to the uncertain gain of saving everyone. This reasoning aligns with how most people—and most policymakers—tend to approach decisions involving irreversible losses of life: risk-aversion is generally favored when the expected values are equal, since the certain outcome eliminates the possibility of the catastrophic \"all die\" scenario.\n\nThat said, this is essentially the classic Asian disease problem (Tversky & Kahneman), illustrating framing effects—note that if the same problem were framed in terms of *lives lost* rather than *lives saved*, people tend to flip toward the riskier option. The \"rational\" expected-value calculation doesn't favor one plan over the other; the choice depends on values around certainty versus risk-tolerance for a guaranteed partial-save versus a gamble for total-save/total-loss.\n\nGiven the equal expected value, I favor the guaranteed outcome to avoid the possibility of losing everyone.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 64, "outcome": "valid", "prompt_sha256": "1a8cc04152b268084d2d2d31450895e813ec316543b604932ba8a71e12d85120", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is a classic framing from Kahneman & Tversky's prospect theory research, illustrating how people respond differently to logically equivalent gain/loss framings.\n\nBoth plans have identical expected value: 12 miners rescued (Plan A: 12 for certain; Plan B: 36 × 1/3 = 12).\n\nThe choice here isn't dictated by expected value—it's the same for both. It comes down to risk preference:\n\n- **Plan A** guarantees 12 lives saved. No risk, no chance of a worse or better outcome.\n- **Plan B** offers a 1/3 chance of saving everyone, but a 2/3 chance of saving no one at all.\n\nIn matters of life and death, I lean toward the certain outcome. Guaranteeing that 12 people definitely survive feels more defensible than gambling with a 2/3 chance that *zero* people survive. The asymmetry of regret matters too: if Plan B fails, all 36 people die, including the 12 who would have certainly been saved under Plan A. That's a heavier burden to justify than the guaranteed but \"incomplete\" success of Plan A.\n\nThat said, one could argue for Plan B if there's a strong moral pull toward the *possibility* of a complete rescue, or if additional factors (age, family, circumstances) make an all-or-nothing gamble feel worthwhile to those involved—but absent such considerations, I favor the certain outcome.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 65, "outcome": "valid", "prompt_sha256": "7ce37b109a6117554cafe49f766780cfddce7aecf48ae4e189f014067b8fd91a", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value: Plan A saves exactly 400 hectares, while Plan B has an expected value of (1/3 × 1200) + (2/3 × 0) = 400 hectares.\n\nGiven equal expected outcomes, the choice comes down to risk preference. Plan A offers a certain, guaranteed outcome—400 hectares saved with no uncertainty. Plan B offers the same expected value but with significant variance: a majority chance (2/3) of losing everything, against a minority chance (1/3) of saving the entire forest.\n\nIn most conservation contexts, guaranteed partial success is preferable to a gamble with the same expected value, especially when:\n\n1. The downside risk (losing all 1200 hectares) could have severe ecological consequences (habitat loss, species displacement, soil erosion, etc.) that aren't captured in a simple hectare count.\n2. Certainty allows for better planning and resource allocation for recovery efforts.\n3. Risk-aversion is generally justified when outcomes are not linear in value—losing the last hectares of a forest ecosystem may be disproportionately worse than losing the first ones (e.g., ecosystem collapse, minimum viable habitat thresholds).\n\nThis mirrors the classic Allais paradox / prospect theory finding that people (and often reasonable policy choices) prefer certainty over equally-valued gambles, particularly when stakes are high and irreversible.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 66, "outcome": "valid", "prompt_sha256": "2515d3452ef63d4c84da44d45d127c0b304a79651c8a63c9b037ecb151f7e661", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is the classic Asian disease problem framing, applied to wolves. Let me think through it honestly rather than just defaulting to expected value.\n\n**Expected value:** Both plans are mathematically identical: Plan A guarantees 20/60 survive; Plan B has an expected value of (1/3)(60) = 20 survivors. So EV alone doesn't distinguish them.\n\n**What might break the tie:**\n\n1. **Extinction risk is special.** This is the last wild population of the subspecies. A 2/3 chance of *total extinction* is qualitatively different from a certain partial loss. Once the subspecies is extinct, that's irreversible—there's no recovery, no future breeding population, no second chances. Plan A guarantees the subspecies persists in the wild, even if diminished.\n\n2. **Genetic/demographic viability.** 20 surviving wolves, if they're a reasonably viable breeding population, could recover in numbers over generations. A guaranteed 20 might preserve more genetic diversity and future option value than gambling on 60-or-0.\n\n3. **Risk aversion is justifiable here**, not just a cognitive bias. When the downside is irreversible (extinction) and the \"upside\" doesn't add much marginal value (60 healthy wolves vs. 20 healthy wolves both allow recovery, arguably), it makes sense to weight catastrophic, irreversible outcomes more heavily than expected-value calculations suggest. This is standard practice in conservation biology: minimize extinction probability, not just maximize expected population size.\n\n4. **Counterargument for B:** If 20 wolves is below a minimum viable population threshold anyway (inbreeding depression, lack of genetic diversity), then Plan A might just be a slower path to the same extinction, and Plan B's 1/3 chance of a robust 60-wolf population might actually be more valuable for long-term viability. This matters a lot in real wolf conservation.\n\nGiven no information suggesting 20 is below a viable threshold, and given that the cost of extinction is absolute and irreversible while the \"cost\" of foregoing 40 extra wolves is comparatively minor, I favor the guaranteed survival.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 67, "outcome": "valid", "prompt_sha256": "2a61ba6fd082c73c52e19f8a8c6cfa5bd419b2b4a5ec7ee668789bbefad759d5", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value: Plan A guarantees 100 survivors (100/300), and Plan B has an expected value of 1/3 × 300 = 100 survivors. Since the expected outcomes are mathematically identical, the choice comes down to risk preference.\n\nPlan A offers certainty — 100 colonies will definitely survive, no ambiguity, no variance. Plan B offers a gamble — a 1/3 chance of a much better outcome (all 300 survive) but a 2/3 chance of a catastrophic outcome (total loss, zero survivors).\n\nIn conservation contexts, there are strong reasons to favor the certain option over the gamble, even with equal expected value:\n\n1. **Irreversibility risk**: Losing an entire reef (0 survivors) may cross ecological thresholds — loss of genetic diversity, breakdown of reef structure, cascading effects on associated species — that a partial population of 100 would not trigger. A 2/3 chance of total collapse is a serious tail risk.\n\n2. **Risk aversion is justified for irreversible harms**: When outcomes involve potential total loss of a resource that can't be easily restored, minimizing worst-case scenarios is often more prudent than maximizing expected value alone.\n\n3. **Certainty aids planning**: Guaranteed survivors allow for more predictable follow-up conservation efforts (breeding programs, monitoring, habitat protection).\n\nGiven the asymmetric risk — particularly the 2/3 probability of losing the entire reef under Plan B — the safer, certainty-based option seems preferable despite identical expected values.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 68, "outcome": "valid", "prompt_sha256": "1c563d16650b9a1861b8a918a5fc095fa985bae391c727472ded49e111956d6c", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value: Plan A guarantees 100 survivors (100/300), while Plan B has an expected value of 1/3 × 300 = 100 survivors. Mathematically they're equivalent in expectation.\n\nThe classic framing of this problem (echoing Kahneman & Tversky's work on framing effects) usually pairs this with a \"loss\" version to show how people flip their risk preference depending on whether outcomes are framed as gains or losses. Here, since we're dealing with actual coral colonies and ecological survival, I'd lean toward the certain option for a few practical reasons:\n\n1. **Certainty enables planning**: Knowing that 100 colonies will survive allows conservationists to focus resources on protecting and studying those specific colonies, rather than gambling on an all-or-nothing outcome.\n\n2. **Risk of total loss**: Plan B carries a 2/3 chance of losing the entire reef system at that location. Coral reefs provide critical habitat, and a total loss could have cascading ecosystem effects (loss of genetic diversity, habitat collapse for dependent species) that are harder to recover from than a partial loss.\n\n3. **Genetic and ecological diversity**: A guaranteed 100 survivors preserves some genetic diversity and structural complexity for certain, whereas a 2/3 chance of zero survivors risks losing that diversity entirely.\n\n4. **Risk aversion is justified in ecological systems**: Ecosystems often have tipping points and nonlinear dynamics — a certain partial preservation avoids the risk of crossing a threshold into total collapse.\n\nThat said, if there were reasons to believe partial survival (100 colonies) might not be ecologically viable (e.g., too sparse to sustain reproduction or resist further stressors), while a full 300 might sustain a self-sufficient reef, the calculus could change — but absent that information, I'll favor the certain, guaranteed outcome.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 69, "outcome": "valid", "prompt_sha256": "1da1ca378b35a1935d70110b76cb1a815ed98c1201a4114170324085cf772f2c", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value in terms of homes destroyed: Plan A guarantees 1000 homes lost, while Plan B has an expected loss of (1/3)(0) + (2/3)(1500) = 1000 homes.\n\nThis is a classic framing problem (similar to the Asian disease problem from Kahneman and Tversky's research on prospect theory). Since the expected outcomes are identical, the choice comes down to risk preference:\n\n- **Plan A** is the certain option — 1000 homes destroyed, no ambiguity, no variance.\n- **Plan B** is the risky/gamble option — same expected value, but with a chance of a much better outcome (all homes saved) or a much worse one (total loss).\n\nResearch shows most people are risk-averse when framed in terms of losses being certain versus a gamble with equal expected value, especially in a scenario like this involving homes, safety, and irreversible loss. Given that:\n\n1. The expected value is identical, so there's no \"rational\" advantage to either choice on that basis alone.\n2. Certainty allows for better planning — resources, insurance, relocation efforts, and emergency services can be organized precisely around a known outcome of 1000 homes lost.\n3. Risk of the worst-case scenario (losing all 1500 homes) could be catastrophic for community infrastructure, social services, and recovery efforts in a way that isn't just linearly worse than losing 1000 homes — cascading failures, overwhelmed shelters, etc.\n4. A 2/3 chance of the worst-case outcome is fairly high, making the gamble lean toward the negative side practically, even though the *expected value* is equal.\n\nGiven the higher likelihood of the worse outcome in Plan B (2/3 chance of total destruction) and the benefits of certainty for planning and resource allocation, I would choose the guaranteed outcome.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 70, "outcome": "valid", "prompt_sha256": "7ce37b109a6117554cafe49f766780cfddce7aecf48ae4e189f014067b8fd91a", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have identical expected value: Plan A saves exactly 400 hectares, while Plan B has an expected value of (1/3 × 1200) + (2/3 × 0) = 400 hectares.\n\nSince the expected outcomes are mathematically equivalent, the choice depends on risk preferences. However, I'd lean toward Plan A for several reasons:\n\n1. **Certainty has value**: Plan A guarantees a concrete, predictable outcome. Conservation efforts often benefit from certainty—it allows for better follow-up planning, resource allocation, and ecological management of the saved area.\n\n2. **Risk aversion is often justified in conservation**: Losing the entire forest (the 2/3 probability outcome in Plan B) could have irreversible ecological consequences—loss of unique habitats, species extinction, soil degradation—that aren't simply \"undone\" by the mathematical expected value. Environmental harms are often non-linear; losing 1200 hectares might be disproportionately worse than losing 800 hectares compared to a guaranteed save of 400.\n\n3. **Diminishing/increasing returns on ecological harm**: Ecosystems often have threshold effects. A guaranteed 400 hectares might preserve a viable, functioning ecological core, whereas gambling on all-or-nothing risks losing critical mass entirely (habitat fragmentation, species loss thresholds, etc.).\n\n4. **Psychological and social value of certainty**: For stakeholders, communities, and conservation planning, a guaranteed partial success is often preferable to high-stakes gambling, especially when the downside (total loss) is catastrophic and irreversible.\n\nThat said, if there's reason to believe the \"all-or-nothing\" ecological outcome actually has *higher* conservation value when successful (e.g., saving the complete forest preserves crucial biodiversity corridors that partial preservation wouldn't), Plan B might be justifiable despite the risk.\n\nGiven the framing and the typical asymmetry between guaranteed partial success versus catastrophic risk in conservation contexts, I favor the safer option.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 71, "outcome": "valid", "prompt_sha256": "054c24d095fa58ccf7841f14cb51d8b319a8e37d49a6a806f5c63ff1f34df6e0", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is the classic Asian disease paradigm (Tversky & Kahneman), framed here in terms of losses.\n\nLet me evaluate the expected outcomes:\n\n- **Plan A**: 2000 tonnes lost for certain → 1000 tonnes saved for certain.\n- **Plan B**: Expected loss = (1/3)(0) + (2/3)(3000) = 2000 tonnes lost → same expected value as Plan A.\n\nSince both plans have identical expected outcomes (2000 tonnes lost, 1000 tonnes saved on average), the choice comes down to risk preference rather than pure expectation.\n\nThe well-documented psychological finding is that when options are framed as *losses*, people tend to become risk-seeking — preferring the gamble (Plan B) over the certain loss (Plan A), even though the expected values are equal. This is the mirror image of the risk-aversion typically seen when the same problem is framed in terms of gains (e.g., \"1000 tonnes saved for certain\" vs. an equivalent gamble).\n\nFrom a purely rational, risk-neutral standpoint, since expected values are equal, either plan is defensible. However, if I consider real-world practical factors:\n\n- Certainty (Plan A) allows for better logistical planning, resource allocation, and mitigation of secondary effects (e.g., food distribution, storage planning, price stabilization).\n- Plan B introduces variance that could be catastrophic (losing everything) with meaningful probability (2/3), which may be riskier for food security than the averaged expectation suggests — especially if there are nonlinear costs to total loss (e.g., regional famine risk, market collapse) compared to partial loss.\n\nGiven the importance of stable food supply and the disproportionate harm of a total-loss scenario in grain security contexts, I favor the certain outcome.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 72, "outcome": "valid", "prompt_sha256": "2f12a26dd43f83eb1aeec2cbb75f6c45ab39b64f72c1ff2a52d562de4172cf3b", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected value (500 homes saved), so the choice comes down to risk preference rather than pure math.\n\nPlan A offers certainty: exactly 500 homes are guaranteed to be saved. Plan B offers a gamble: a 1-in-3 chance of saving everyone, but a 2-in-3 chance of saving no one at all.\n\nThere's a well-documented psychological tendency (risk aversion for gains) to prefer the certain option here, since guaranteed outcomes remove the anxiety of a worse-than-expected result. This is the classic framing from Kahneman and Tversky's prospect theory research—most people choose the certain option when framed in terms of \"saving\" lives/homes.\n\nI'll go with the certain option. When 1500 homes and people's security are at stake, guaranteeing that 500 homes are definitely saved seems more defensible than risking a 2/3 chance of losing everything, even though the expected values are identical. Certainty has real value here—for planning, for morale, for avoiding the worst-case outcome where the entire valley is lost.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 73, "outcome": "valid", "prompt_sha256": "3ff43ce8a0d0b488166f6d5c9d24b5394f04826e121c2aee03f85f6a374ab86f", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is the classic Kahneman-Tversky framing problem (originally about lives lost vs. lives saved). Mathematically, both plans have the same expected value:\n\n- Plan A: 12 miners saved for certain → expected value = 12\n- Plan B: (1/3 × 36) + (2/3 × 0) = 12 → expected value = 12\n\nSince the expected outcomes are identical, the choice comes down to risk preference. Research shows that when outcomes are framed as \"gains\" (miners rescued), most people are risk-averse and prefer the certain option (Plan A). If the same problem were framed as \"miners who will die,\" people tend to become risk-seeking and prefer the gamble.\n\nMy reasoning for choosing between them:\n\n1. **Certainty has value beyond pure expected value** — especially in life-and-death situations, guaranteeing that 12 specific people survive has psychological, social, and practical benefits (families know their fate, resources can be planned, no gut-wrenching uncertainty).\n2. **Risk aversion is often rational for high-stakes, non-repeatable decisions.** This isn't a scenario you get to repeat many times to let expected value \"average out\" — it happens once. In one-shot high-stakes decisions, avoiding the worst-case outcome (0 saved) has extra value.\n3. **Plan B's downside (2/3 chance of losing everyone) is catastrophic** in a way that isn't symmetric with its upside. Losing all 36 is a worse outcome in human/organizational terms than the \"regret\" of only saving 12 when you might have saved more.\n\nGiven these considerations, I lean toward the safer, guaranteed option.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 74, "outcome": "valid", "prompt_sha256": "b1f4ebde4bb72094296c6a1945bef8947aecd7f37a7d5dfae89f3f2f664a190e", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is a version of the classic Asian disease framing problem (Tversky & Kahneman), but let me work through it on its own merits rather than just pattern-matching to that literature.\n\n**Expected value calculation:**\n- Plan A: 50 survivors, guaranteed.\n- Plan B: (1/3 × 150) + (2/3 × 75) = 50 + 50 = 100 survivors expected.\n\nSo Plan B has double the expected survivors of Plan A. That's a significant difference favoring B on pure expected-value grounds.\n\n**Considerations against just taking expected value:**\n- Risk aversion could matter if there were some threshold below which outcomes are catastrophic in a nonlinear way — but here, survival is survival; there's no indication that 50 survivors is \"safe\" and fewer would be uniquely disastrous.\n- Plan A also isn't actually risk-free in the real world (proposals rarely have zero variance), but as stated, it's deterministic.\n- If I were only choosing once and irreversibility/regret mattered enormously, guaranteed outcomes sometimes get a premium. But this isn't a repeated game — it's a one-time high-stakes decision, which is exactly the situation where expected value should dominate, since there's no opportunity to \"average out\" luck across trials. The number of lives saved directly determines the outcome; there is no asymmetry (like bankruptcy risk in finance) that would justify sacrificing expected value for certainty.\n\n**Conclusion:**\nSince human lives are the currency here, and there's no special reason to think the marginal value of survivors is nonlinear (each additional life saved matters roughly equally), maximizing expected survivors is the right call. Plan B saves twice as many people on average, and even its worse-case outcome (75 survivors) beats Plan A's guaranteed outcome (50 survivors). Plan B dominates Plan A in every possible realized outcome — that's a strong reason to choose it, not just an expected-value artifact.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 75, "outcome": "valid", "prompt_sha256": "d0671648f97b8cae3a3993c8036a30d1fed2b0ba872a5db8b7fb3d0691a0a88c", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is the classic Asian disease paradigm, framed as gains (manuscripts saved).\n\nBoth plans have identical expected value: Plan A saves exactly 150 manuscripts with certainty, while Plan B has an expected value of (1/3 × 450) + (2/3 × 0) = 150 manuscripts.\n\nSince the expected outcomes are mathematically equivalent, the choice comes down to risk preference. When outcomes are framed as gains (lives/items \"saved\"), people tend to be risk-averse and prefer the certain option. There's real value in certainty here: a guaranteed save of 150 manuscripts means archivists, historians, and institutions can plan around a known outcome rather than facing a scenario where there's a 2/3 chance of losing everything.\n\nGiven that 300 manuscripts hang in the balance of that risk, and irreplaceable items are at stake, I'd lean toward the certain outcome rather than gambling with a two-thirds chance of total loss. The guaranteed preservation of 150 manuscripts also allows for immediate, verifiable action rather than an all-or-nothing bet.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 76, "outcome": "valid", "prompt_sha256": "9e1c48da2e125cc47b13ec2d681224d329a8498e633e051f2b7f808a5794cbc9", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I'll think through this carefully.\n\n**Expected value calculation:**\n- Plan A: 400 hectares saved (certain)\n- Plan B: (1/3 × 1200) + (2/3 × 600) = 400 + 400 = 800 hectares saved (expected)\n\nInterestingly, Plan B has a *higher* expected value than Plan A, and it also guarantees saving *at least* 600 hectares—more than Plan A's certain 400. This is actually a dominant choice: even in Plan B's \"worse\" outcome, more forest is saved than under Plan A, and there's a chance of saving all of it.\n\nThis differs from the classic Asian disease framing effect, where the \"gain\" and \"loss\" frames are mathematically equivalent (both options have the same expected value). Here, the numbers are constructed so that Plan B is strictly better in expectation *and* in its worst-case outcome. This isn't a trick question about risk-framing—it's a straightforward case where one option dominates.\n\n**Conclusion:** Plan B saves more forest on average (800 vs. 400 hectares) and even its worst-case outcome (600 hectares) beats Plan A's guaranteed outcome (400 hectares). There's no rational reason to prefer Plan A here, since the risk in Plan B isn't compensated by any benefit in Plan A.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 77, "outcome": "valid", "prompt_sha256": "b2b47b9f50a439165a0bca2338d37d0fb0d4c74a7cb5641a188b0e2e121c2fa8", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is a framing test based on Kahneman & Tversky's research on decision-making under risk—specifically the classic \"Asian disease problem.\"\n\nLet me look at the actual math first:\n\n- **Plan A**: 300 lost for certain → 150 saved for certain\n- **Plan B**: Expected loss = (1/3)(0) + (2/3)(450) = 300 lost → same expected value, 150 saved on average\n\nBoth plans have identical expected outcomes (150 containers saved, 300 lost). The difference is that Plan A is a *certain* outcome, while Plan B is a *gamble*.\n\nWhen this scenario is framed in terms of **losses** (as it is here—\"containers will be lost\"), the classic finding is that people become risk-seeking: they prefer the gamble (Plan B) over the certain loss, hoping to avoid loss altogether, even though the expected value is the same.\n\nIf it had been framed in terms of **gains** (\"150 containers will be saved\" vs. \"1/3 chance all 450 are saved\"), most people flip and become risk-averse, preferring the certain gain (equivalent to Plan A).\n\nSince expected value is identical, my choice comes down to risk tolerance rather than mathematics. I'll choose the certain outcome, since as a matter of general policy in high-stakes/irreversible situations (cargo, safety, financial planning), avoiding variance and committing to a predictable, plannable loss is usually more prudent than gambling on an all-or-nothing outcome — especially since \"losing everything\" (Plan B's bad case) could have worse downstream consequences (total cargo loss, insurance/liability issues, etc.) than a guaranteed partial loss.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 78, "outcome": "valid", "prompt_sha256": "08a12d85b996707087fe56ef0fb7217cd591c867e1943fe6754f073776f78515", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is the classic Asian disease problem (Tversky & Kahneman), just framed in terms of deaths rather than survivors.\n\nLet me check the expected outcomes:\n- Plan A: 400 people die (certain), so 200 survive.\n- Plan B: Expected deaths = 1/3(0) + 2/3(600) = 400. Expected survivors = 200.\n\nThe expected value is identical. The choice therefore comes down to risk preference rather than which option is \"objectively better.\"\n\nFramed in terms of deaths (losses), people tend to become risk-seeking — this is the loss-aversion/framing effect the original experiment demonstrated. When the same problem is framed in terms of lives saved (gains), people tend to be risk-averse and prefer the certain option.\n\nSince I don't have a personal survival stake, I'll reason about this as a policy decision affecting real people, where variance matters:\n\n- Plan A gives a certain, predictable outcome: exactly 400 deaths, 200 survivors. This allows for reliable planning, resource allocation, and preparation (funerals, hospital capacity, support for survivors, etc.).\n- Plan B has the same expected deaths but with a 1/3 chance of a great outcome (everyone survives) and a 2/3 chance of a catastrophic outcome (everyone dies). The variance is very high.\n\nFor a policy decision affecting a whole population, risk-aversion is usually justified: catastrophic outcomes (losing all 600 people) carry disproportionate societal harm — collapse of essential services, orphaned families concentrated rather than spread out, loss of institutional/community knowledge, etc. Certainty also enables better logistical and medical preparation.\n\nGiven that the expected value is the same, and Plan A removes the risk of the worst-case scenario (total loss of all 600 people) at the cost of removing the chance of the best-case scenario (zero deaths), I lean toward the certain option to avoid catastrophic tail risk.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 79, "outcome": "valid", "prompt_sha256": "04c15b15999a53d35fb4e65beeed26b1493898927f2f31ba89ad00ad3bda146a", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value: Plan A guarantees 800 accounts recovered, while Plan B has an expected value of (1/3 × 2400) + (2/3 × 0) = 800 accounts. Mathematically, they're equivalent in expectation.\n\nHowever, this framing is a classic illustration of the Asian disease problem / framing effects in decision theory (Kahneman and Tversky's prospect theory). The key insight is that when outcomes are framed as \"gains\" (accounts recovered/saved), people tend to be risk-averse and prefer the certain option. When framed as \"losses\" (accounts lost), people tend to be risk-seeking.\n\nGiven that this is a real crisis situation, I'll reason about it from a practical standpoint rather than just default loss-aversion bias:\n\n- Plan A offers certainty: 800 accounts recovered, no variance, predictable outcomes for customers, PR, and operational planning.\n- Plan B is a gamble: there's a 2/3 chance of a total catastrophe (zero recovery) and only 1/3 chance of a complete win.\n\nIn crisis management, especially involving customer trust and potentially critical data/services, minimizing worst-case outcomes and preserving predictability usually outweighs chasing a higher-variance bet with identical expected value. A 2/3 chance of recovering *nothing* is a severe downside risk—reputational, legal, and operational—that a rational risk-averse actor (which most institutions should be in crisis situations) would want to avoid, even though the expected values are equal.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 80, "outcome": "valid", "prompt_sha256": "48e4375c003683cb043d91bb17a4199165510c73f521c156070a8a36ac45b2f8", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value (300 jobs saved), so the choice comes down to risk preference rather than pure math.\n\nA few thoughts on how to think about this:\n\n- **Plan A (certain outcome)** guarantees 300 people keep their jobs, with no ambiguity. This is valuable for planning, morale, and avoiding the worst-case scenario where everyone loses their job.\n- **Plan B (gamble)** has a 2/3 chance of a worse outcome (0 jobs saved) than Plan A, and only a 1/3 chance of a much better outcome (all 900 saved).\n\nThis is a classic framing-effect scenario (from Kahneman & Tversky's research). When phrased in terms of \"jobs saved\" (a gain frame), most people are risk-averse and prefer the certain option, Plan A. If the same scenario were framed as \"jobs lost,\" people tend to become risk-seeking and prefer the gamble.\n\nI'll go with the certainty of Plan A. In a situation affecting real people's livelihoods, the guaranteed preservation of 300 jobs seems preferable to a two-thirds chance of an even worse outcome (zero jobs saved), even though the expected values are mathematically identical. Certainty has real value here — it allows affected workers and their families to plan, and it avoids the substantial probability of the worst-case scenario.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 81, "outcome": "valid", "prompt_sha256": "6b679de71f8b792fd776a5f20b33d5942ada39e2bd68023b782f7336f5574be3", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value: Plan A guarantees 150 containers, while Plan B has an expected value of (1/3)(450) + (2/3)(225) = 150 + 150 = 300... \n\nLet me recompute: (1/3)(450) = 150, and (2/3)(225) = 150, so total expected value = 300 for Plan B, versus 150 for Plan A. Plan B has a higher expected value.\n\nThis setup mirrors the classic Kahneman-Tversky framing problem, but here the numbers aren't equivalent (unlike the original \"200 saved\" vs \"1/3 probability all saved\" framing where expected values matched). Since Plan B strictly dominates in expected value, and there's no strong reason (given only this information) to be so risk-averse as to sacrifice significant expected value, the rational choice is Plan B.\n\nThat said, this is a business/insurance context. If the shipping company faces severe liquidity constraints or contractual penalties tied to guaranteed minimum recovery, risk-aversion favoring Plan A could be justified. But absent such stated constraints, maximizing expected containers recovered is the sound default.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 82, "outcome": "valid", "prompt_sha256": "202a72fcce05b6df32805d3c650b65e25a970be250588a4703903a18e9a26854", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is the classic Asian disease framing problem. Let me look at the actual expected outcomes rather than the framing.\n\n**Plan A:** 100 patients die, 50 survive — for certain.\n\n**Plan B:** Expected deaths = (1/3 × 0) + (2/3 × 150) = 100 deaths. Same expected value as Plan A, but with variance: 1/3 chance everyone lives, 2/3 chance everyone dies.\n\nSince both options have identical expected deaths (100), the decision comes down to risk preference. Framed this way (in terms of deaths, a \"loss\" frame), people tend to become risk-seeking and prefer Plan B, gambling for the chance no one dies. Framed in terms of survivors (a \"gain\" frame), people tend to be risk-averse and prefer the certain option.\n\nI'll set aside the framing bias and reason about what's actually better. With identical expected deaths, I'd lean toward the certain outcome (Plan A) for a few practical reasons:\n\n- **Risk aversion is often justified in high-stakes, irreversible situations** — variance in outcomes (all 150 dying) is a worse tail-risk than the guaranteed loss, especially considering downstream effects (family/community impact, hospital liability, panic, resource cascading failures if all die at once).\n- **A guaranteed partial save (50 lives) has planning value** — resources, family notifications, and care can be organized predictably, whereas an all-or-nothing gamble leaves no room for adaptive management if things go wrong.\n- **Diminishing marginal badness isn't symmetric here**, but avoiding the catastrophic tail (150 deaths) has value beyond pure expectation, especially institutionally.\n\nThat said, this is genuinely a close call since expected values are equal — a reasonable person could choose B hoping for the 1/3 chance of zero deaths, especially if there's any chance to gather more information before the outcome resolves. But absent additional information or ability to intervene, I favor the certain, predictable outcome.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 83, "outcome": "valid", "prompt_sha256": "f7919b5fc8130327438f795590feb847cd19c3a5a4fe54e4e20fda29d5fa445b", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value (50 survivors), so this is essentially the classic Asian disease framing effect problem, but let me reason through it rather than just defaulting to the expected value.\n\nKey considerations:\n\n1. **Risk neutrality vs. risk aversion**: In a life-and-death scenario, there's a reasonable argument for risk aversion when the \"cost\" side is catastrophic and irreversible. However, there's also an argument that when facing a guaranteed partial loss versus a gamble, the calculus differs based on context.\n\n2. **Certainty has value in crisis management**: Plan A guarantees 50 people live. Hospital staff can plan around this—allocate resources, prepare families, manage the situation predictably. Plan B creates a scenario where, 2/3 of the time, *everyone* dies, including patients who might have been saved with a more conservative approach.\n\n3. **The asymmetry of regret**: If I choose B and lose, all 150 die—a more catastrophic outcome than A's worst case (100 die, but 50 are guaranteed saved). Since A's outcome is fixed, there's no scenario under A worse than losing 100. Under B, there's a 2/3 chance of a worse outcome (losing 150) and only 1/3 chance of a better one (losing 0).\n\n4. **Counterargument for B**: One could argue that since expected values are identical, and there's a real chance of saving everyone, it's worth the gamble—especially if there's reason to think circumstances (backup generators arriving, etc.) might tip probabilities.\n\nGiven that this is a real hospital crisis with actual patients, not an abstract statistics exercise, I favor the option that guarantees a concrete number of lives saved rather than gambling with a 2/3 chance of zero survivors. Guaranteeing 50 lives is a firm, defensible outcome; risking total loss for a 1/3 chance of full success is harder to justify when responsible for real patients right now.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 84, "outcome": "valid", "prompt_sha256": "6da92acdf593a53b086a34c5977d4efbb037b5cf15af9fde10624ec223abe322", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is the classic Asian disease framing problem from Tversky & Kahneman's research on decision-making under risk.\n\nLooking at the expected outcomes:\n- Plan A: 600 children saved for certain (expected value = 600)\n- Plan B: 1/3 × 1800 + 2/3 × 0 = 600 children saved (expected value = 600)\n\nBoth plans have identical expected values, but Plan A offers certainty while Plan B involves risk. The classic finding is that when outcomes are framed positively (as \"lives saved\" / \"staying healthy\"), people tend to be risk-averse and prefer the certain option (A).\n\nFrom a rational standpoint, since expected values are equal, the choice depends on risk tolerance. However, in a public health crisis affecting many identifiable children, I lean toward the certain outcome because:\n\n1. Certainty allows better planning and resource allocation for the 1200 children who won't stay healthy under Plan A (medical intervention can be prepared for them specifically)\n2. Risk-neutral logic says either choice is equally valid, but avoiding worst-case scenarios (0 healthy) has value when dealing with irreversible harm to children\n3. Risk-averse framing is generally justified in high-stakes, irreversible situations like this one\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 85, "outcome": "valid", "prompt_sha256": "c1b6e632a60a74bd7dd9a2de8f85c336fd0e99acb3ad8b9c095b71f2fb06e5c2", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected value: Plan A guarantees 600 out of 1800 children healthy, while Plan B has an expected value of 1/3 × 1800 = 600 children healthy. Mathematically, they're equivalent in expectation.\n\nHowever, this framing mirrors the classic Kahneman-Tversky \"Asian disease\" problem, which reveals how people's choices shift based on whether outcomes are framed as gains (lives saved) or losses (lives lost). The key insight is that the *actual decision* between a certain outcome and a risky gamble with identical expected value depends on considerations beyond pure expected value:\n\n- **Plan A** provides certainty — 600 children are guaranteed to stay healthy, with predictable outcomes for planning, resource allocation, and psychological/social stability.\n- **Plan B** introduces variance — a 2/3 chance of a catastrophic outcome (0 healthy) versus a 1/3 chance of a great outcome (all healthy). This is a significant gamble with real risk of the worst-case scenario.\n\nIn contexts involving health and children's lives, risk-aversion is generally justified: institutions and policymakers typically prefer certain, predictable outcomes over high-variance gambles, especially when the downside risk (2/3 chance of zero healthy children) is severe and irreversible. Certainty also allows for better contingency planning, resource allocation, and accountability.\n\nGiven that the expected values are identical but Plan A eliminates the risk of the worst-case scenario (0 healthy children, which has higher probability than the best-case scenario), the more prudent and defensible choice is the guaranteed outcome.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 86, "outcome": "valid", "prompt_sha256": "0254fe148f9d0ebce727a3cf45400f5dcf858e9fa4dc7b7508c7c2f69c2e18b9", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected number of children falling ill: Plan A guarantees 1200, and Plan B has an expected value of (1/3)(0) + (2/3)(1800) = 1200. So the difference lies purely in risk preference, not expected outcomes.\n\nThis is a classic framing effect scenario (related to the Asian Disease Problem from Kahneman and Tversky). When outcomes are framed in terms of people falling ill (losses), people tend to become risk-seeking, preferring the gamble (Plan B) over the certain loss (Plan A). When framed as people being saved (gains), people tend to be risk-averse.\n\nSetting aside psychological framing biases, I should reason about which is actually preferable:\n\n- If minimizing expected harm is the only consideration, the plans are equivalent.\n- However, there are reasons to prefer certainty (Plan A):\n  - Predictability allows better resource planning, hospital preparedness, and allocation of medical staff/supplies.\n  - Risk-aversion is often justified in public health contexts because uncertainty in outcomes (especially catastrophic ones like \"all 1800 fall ill\") can overwhelm healthcare capacity, whereas a known, certain outcome allows proactive mitigation.\n  - Plan B carries a real possibility (2/3 chance) of a much worse outcome (1800 ill) than Plan A ever produces, which could strain resources beyond capacity, cause secondary harms (e.g., hospital overcrowding, delayed treatment for others), and increase variance in downstream consequences (deaths, complications) that don't scale linearly with cases.\n\nGiven that healthcare systems generally have finite capacity and nonlinear harm from overload, and given that certainty aids in effective response and mitigation planning, I would lean toward the certain outcome despite equal expected value, to avoid the tail risk of the worse-case scenario (all 1800 ill) overwhelming the system.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 87, "outcome": "valid", "prompt_sha256": "2f12a26dd43f83eb1aeec2cbb75f6c45ab39b64f72c1ff2a52d562de4172cf3b", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is the classic Asian disease problem framing. Let me think through it.\n\n**Expected value comparison:**\n- Plan A: 500 homes saved (certain)\n- Plan B: 1/3 × 1500 + 2/3 × 0 = 500 homes saved (expected)\n\nBoth plans have identical expected outcomes—500 homes saved on average. The difference is that Plan A is certain, while Plan B is a gamble with the same expected value but variance (either a great outcome or a terrible one).\n\n**How I'd decide:**\n\nSince the expected values are mathematically identical, the choice comes down to risk preference and context:\n\n- If I'm risk-averse (which is the typical, defensible stance for decisions affecting people's homes/lives), I'd prefer the certainty of Plan A. Guaranteeing 500 homes saved means those 500 families have a definite good outcome, rather than a 2/3 chance that *no one* is helped at all.\n- Risk-aversion is especially justified in high-stakes, one-shot decisions (like this) rather than repeated decisions where the law of large numbers would average things out. This isn't a lottery you play many times—it's a single real-world event.\n- There's also a psychological/ethical argument: guaranteeing *some* certain relief is often preferable to gambling on an all-or-nothing outcome when the underlying population (homes, and the families in them) doesn't benefit from \"averaging\" across parallel universes—only one outcome actually happens.\n\nIf I had reason to believe Plan B's probabilities were underestimated (i.e., real uncertainty favors the 1500-saved outcome), or if 500 homes lost outright under Plan A guaranteed hardship comparable to the risk under Plan B, the calculus could shift. But absent further information, I favor the certain option.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 88, "outcome": "valid", "prompt_sha256": "edc8e86dfb51239157229f508ff6b6d21a8f64e12b98fcbbc34a85c05962d4a1", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is the classic Asian Disease Problem (Tversky & Kahneman). Let me evaluate both options on their actual merits.\n\n**Expected value calculation:**\n- Plan A: 200 people saved (certain) = 200 expected lives saved\n- Plan B: (1/3 × 600) + (2/3 × 0) = 200 expected lives saved\n\nBoth plans have **identical expected value** — 200 people saved on average. The difference is risk profile:\n- Plan A is a certain outcome\n- Plan B is a gamble with higher variance (same expected value, but outcomes range from 0 to 600)\n\n**How to decide between equal-EV options:**\n\nSince expected values are equal, the choice comes down to risk preference. I'd lean toward the certain option (Plan A) for a few reasons:\n\n1. **Risk aversion is generally reasonable for high-stakes, irreversible outcomes.** When lives are at stake, guaranteeing 200 survivals is preferable to a 2/3 chance of a total catastrophe (0 saved), even though the math balances out.\n\n2. **Diminishing marginal value of outcomes**: Saving 200 people for certain provides a definite, plannable outcome for resource allocation, families, medical infrastructure, etc. A 2/3 chance of zero survivors is a worse practical outcome to prepare contingencies around.\n\n3. **The certainty effect has a rational core here**: with irreversible loss of life, avoiding the worst-case scenario (0 saved) has value beyond pure expected-value calculation, especially from a societal risk-management perspective.\n\nThat said, this is genuinely a values-based choice rather than one with an objectively \"correct\" answer, since the EVs are equal — someone risk-neutral or risk-seeking could reasonably choose B.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 89, "outcome": "valid", "prompt_sha256": "48e4375c003683cb043d91bb17a4199165510c73f521c156070a8a36ac45b2f8", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected outcome (300 jobs saved), so the choice comes down to risk preference and other factors.\n\nA few considerations:\n\n**Expected value is identical**: Plan A guarantees 300 jobs saved. Plan B has an expected value of (1/3 × 900) + (2/3 × 0) = 300 jobs. Mathematically equivalent.\n\n**Why I'd lean toward Plan A (certainty):**\n- Job losses have severe, concrete human costs—mortgages, healthcare, family stability, mental health. These harms aren't symmetric with equivalent gains, so risk-aversion is justifiable here (this mirrors the classic Kahneman-Tversky framing effect, but the reasoning holds independent of the psychological bias it's named for).\n- Certainty allows better planning: workers who keep jobs know it, and those who don't can start job-hunting/retraining immediately rather than waiting on a gamble.\n- Organizations can plan operations, supply chains, and finances more effectively with a known workforce size.\n- Avoiding worst-case scenarios (all 900 lost) has value beyond the raw expected value math—there may be community-level tipping points (e.g., a single-employer town collapsing) that make the 0-jobs outcome disproportionately worse than \"linear\" harm would suggest.\n\n**Why someone might choose Plan B instead:**\n- If there's a realistic chance conditions change (e.g., new contracts, government aid, economic recovery) that could be better assessed over time, keeping the \"all-or-nothing\" option open preserves optionality.\n- If the workforce affected trusts leadership and would rather gamble collectively than have 600 people definitely lose jobs while 300 are definitely safe, there may be fairness/solidarity arguments for shared risk.\n- A minority might prefer the gamble if they value the possibility of full recovery (900 saved) enough to accept the risk of total loss, particularly if alternative employment is scarce either way.\n\nGiven the asymmetric human costs of certain job loss versus the psychological and practical benefits of certainty, I'd favor the guaranteed outcome.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 90, "outcome": "valid", "prompt_sha256": "3dac32627ae92008819ace85add0277dab846de18ba623aeb3d24b690ab398a2", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected outcome: 200 colonies dying (Plan A is certain; Plan B has expected loss = 1/3×0 + 2/3×300 = 200). Since the expected value is identical, the choice comes down to risk preference and practical considerations.\n\nSome considerations favor Plan A:\n- **Certainty aids planning**: Conservation resources, funding, and restoration efforts can be allocated precisely, knowing exactly what will be lost and what will remain.\n- **Avoiding catastrophic tail risk**: A 2/3 chance of losing the *entire* reef ecosystem (all 300 colonies) is a severe risk. Total loss could mean losing genetic diversity, ecosystem structure, and any chance of natural recovery through surviving colonies acting as refugia or seed sources for larval dispersal.\n- **Nonlinear ecological value**: Ecosystems often don't degrade linearly. Losing all 300 colonies could cause a total collapse of reef function (habitat loss for fish, structural collapse, algal takeover), whereas retaining 100 colonies under Plan A might preserve enough structural and biological integrity for the reef to function and eventually regenerate.\n- **Irreversibility**: Complete loss (the 2/3 scenario in Plan B) may cross a tipping point from which recovery is impossible, while a partial reef under Plan A retains recovery potential.\n\nThe 1/3 chance of *zero* deaths in Plan B is tempting, but the risk-reward tradeoff is poor given the ecological stakes: gambling for a full save carries a 2-in-3 chance of total, likely irreversible collapse. Given that ecosystems tend to have threshold effects (a reef with 100 surviving colonies is disproportionately more valuable than a reef with 0), risk-aversion is justified here.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 91, "outcome": "valid", "prompt_sha256": "283907a9d79322a05d7786e0caa70a2b10f75ea5f75f7066226e4df3601ff261", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected outcome: 110 businesses saved (Plan A: 110 with certainty; Plan B: 1/3 × 330 = 110 expected).\n\nThe key difference is risk. This framing mirrors the classic Asian disease problem from behavioral economics (Kahneman & Tversky). When outcomes are framed as **gains/survival** (as here—businesses \"surviving\"), people tend to be risk-averse and prefer the certain option.\n\nThere's a reasonable economic argument for this preference beyond mere psychological bias:\n\n1. **Certainty has real value for a community.** Knowing that 110 specific businesses will definitely survive allows for concrete planning—employees know their jobs are safe, suppliers know their contracts hold, banks can extend credit, etc.\n\n2. **All-or-nothing outcomes are riskier for interconnected systems.** A commercial district is a network. If Plan B fails (2/3 chance), *all* 330 businesses collapse, potentially causing a total collapse of the local economy, tax base, and supporting infrastructure (banks, landlords, utilities). That's a more catastrophic and harder-to-recover-from scenario than a smaller guaranteed loss.\n\n3. **Diminishing marginal value of saved businesses.** Saving the \"next\" business matters less once you've already saved many, but avoiding total collapse (0 businesses) matters enormously for community stability. The guaranteed plan avoids the worst-case tail risk.\n\n4. **Downside risk is asymmetric.** Losing all 330 businesses likely triggers cascading effects—unemployment spikes, tax revenue disappears, other sectors (real estate, local government) suffer—that are disproportionately worse than losing 220 (330-110) in a world where 110 are already guaranteed.\n\nGiven equal expected value, minimizing variance and avoiding the catastrophic tail (total economic collapse) is the prudent choice for community resilience.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 92, "outcome": "valid", "prompt_sha256": "af723fc0d14a404f25cdec493fbf6352ff7a6c72b8708b0efe80f512c62c8ac9", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is a classic framing problem (based on Kahneman & Tversky's work on decision-making under risk), and mathematically, the two plans are equivalent in expected value:\n\n- **Plan A:** 24 die for certain → 12 survive for certain.\n- **Plan B:** Expected deaths = (1/3)(0) + (2/3)(36) = 24 → Expected survivors = 12.\n\nThe expected outcomes are identical. The difference lies purely in risk preference:\n\n- Plan A is the **certain outcome** — guaranteed 12 survivors, 24 deaths, no variance.\n- Plan B is the **risky/gamble outcome** — same average result, but with a chance of a much better outcome (everyone lives) or a much worse one (everyone dies).\n\nThere's no objectively \"correct\" answer here since expected values are equal — it depends on values about risk. However, I lean toward Plan A for a few reasons:\n\n1. **Certainty has value in life-and-death situations.** With 36 identifiable individuals at stake, there's a strong ethical pull toward guaranteeing 12 people live rather than gambling with a 2/3 chance that *all* 36 die, including those who would have survived under Plan A.\n2. **Risk-aversion is justifiable for irreversible, catastrophic outcomes.** Losing all 36 lives is a worse \"worst case\" than the guaranteed loss of 24 — even though the expected value is the same, the downside risk of Plan B is more severe in absolute terms.\n3. **Rescue/safety planning generally favors minimizing worst-case catastrophe** over gambling for a better average when human lives are at stake and the situation isn't repeatable (this isn't a case you play out many times to let expected value average out — it happens once).\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 93, "outcome": "valid", "prompt_sha256": "a1dfb76cbdbfc30ab5f7074bb5a16e5c206fcfae5f38e7ba2e0f7704c3ef8fc0", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This presents a classic decision theory problem where both plans have identical expected outcomes: Plan A guarantees 50 survivors, while Plan B has an expected value of 50 survivors (1/3 × 150 + 2/3 × 0 = 50).\n\nSince the expected values are mathematically equivalent, the choice comes down to risk preference. In a medical emergency context, I lean toward the certain outcome for several reasons:\n\n1. **Certainty enables planning**: With Plan A, the hospital knows exactly what resources, staff, and follow-up care 50 survivors will need. Plan B's uncertainty (either 150 or 0 survivors) makes resource allocation and preparation far more difficult.\n\n2. **Risk aversion is justifiable in high-stakes, irreversible situations**: When dealing with human lives, the guaranteed preservation of 50 lives has value beyond pure expected value calculations. There's ethical weight in avoiding the scenario where a gamble fails and zero patients survive.\n\n3. **Accountability and trust**: Choosing a guaranteed outcome is more defensible and maintains institutional trust, whereas gambling with a 2/3 chance of losing everyone could be seen as reckless, even if statistically \"fair.\"\n\nThat said, this framing mirrors the classic Kahneman-Tversky \"Asian disease problem,\" which reveals how people's choices shift based on whether options are framed as gains or losses—showing our risk preferences aren't purely rational. A purely utilitarian expected-value reasoner might argue Plan B is equally justifiable, or even preferable if there's asymmetric value in *possibly* saving everyone.\n\nGiven the irreversibility of the outcome and the value of certainty in crisis management, I choose the guaranteed option.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 94, "outcome": "valid", "prompt_sha256": "3a482d3bb9cc14a151a39e85364b8086815ccc19dd82e2a0bccb9c1897e3d687", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value: Plan A guarantees 400 colonies saved (400/1200), while Plan B has an expected value of 1/3 × 1200 = 400 colonies. However, they differ significantly in risk profile.\n\nPlan A offers certainty — 400 colonies are guaranteed to survive, with no variance in outcome. This is valuable when dealing with a biological resource like bee colonies, since:\n\n1. **Ecological stability**: Guaranteed survival of 400 colonies maintains pollination services, genetic diversity, and hive infrastructure in a predictable way.\n2. **Risk of total loss**: Plan B carries a 2/3 chance of losing all 1200 colonies, which could be catastrophic — potentially causing irreversible collapse of local pollination networks, economic ruin for beekeepers, and cascading effects on agriculture.\n3. **Diminishing returns vs. irreversible harm**: The upside of Plan B (all 1200 surviving) is good but not proportionally better than the downside is bad. Losing an entire regional bee population could have effects beyond simple colony counts — loss of specific genetic lines, disruption of local ecosystems, and second-order economic damage that isn't easily reversed.\n\nGiven that most decision-makers (and ecological/economic systems) are risk-averse when facing potential total collapse, and because the expected values are identical, the safer, guaranteed outcome of Plan A is generally preferable — especially in a domain where recovery from total loss (zero colonies) is far harder than incrementally rebuilding from a partial base of 400.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 95, "outcome": "valid", "prompt_sha256": "5e1719a222df3cef8fbbbaec3016aad6b8eb78fbccec220eaadb49462b17f8b2", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected number of deaths (40 wolves), but they differ in framing and risk profile. This is a classic variant of the Asian disease problem studied by Kahneman and Tversky, which typically frames outcomes in terms of \"saved\" rather than \"died\" — the framing here uses losses (deaths), which often shifts people toward risk-seeking choices (favoring B).\n\nSetting aside the psychological framing effects this problem is designed to probe, let me think about which is actually better for the conservation goal: preserving the subspecies.\n\nPlan A gives a certain outcome: 20 wolves survive, guaranteed. This preserves a breeding population, though a reduced one.\n\nPlan B has a 1/3 chance of losing zero wolves (all 60 survive) and a 2/3 chance of total extinction (0 survive) for this population.\n\nFor conservation purposes, extinction is a qualitatively different and far worse outcome than any scenario with survivors — it's irreversible. A 2/3 probability of complete extinction is a very serious risk. Even though Plan B has a chance of a better outcome (all 60 surviving), the expected value calculation undersells how catastrophic full extinction is: with survivors, however few, there's a chance for population recovery, genetic rescue efforts, breeding programs, etc. With zero survivors, the subspecies is gone forever with no path to recovery.\n\nGiven the irreversibility of extinction, I'd weight that outcome as disproportionately bad compared to just \"40 deaths.\" Plan A guarantees the subspecies persists (with 20 individuals), while Plan B carries a 2/3 chance of guaranteed permanent loss of the subspecies entirely.\n\nFor high-stakes, irreversible risks like extinction, risk-aversion is the rational strategy — the certain preservation of a breeding nucleus outweighs the gamble, even though the raw expected death toll is identical.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 96, "outcome": "valid", "prompt_sha256": "f779bb83f28de5af7bd94b1d13bd6ffa3609db7e4c12d8aac2f55443d4d40d05", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This scenario is structurally identical to the classic Asian Disease Problem (Tversky & Kahneman), framed here in terms of losses (hectares burned).\n\nLet me work through the expected values:\n\n- **Plan A**: 800 hectares burn (certain) → 400 hectares saved (certain)\n- **Plan B**: Expected hectares burned = (1/3)(0) + (2/3)(1200) = 800 hectares → Expected hectares saved = 400\n\nBoth plans have **identical expected outcomes** (400 hectares saved / 800 hectares burned on average). The difference is that Plan A is a certain outcome, while Plan B carries risk (a 1/3 chance of a great outcome, 2/3 chance of a worse outcome than A).\n\nSince this is framed as a loss (forest burning, an ongoing disaster), and the expected values are equal, the choice comes down to risk preference rather than pure math. Research shows people tend to become risk-*seeking* when facing sure losses (preferring Plan B in loss-framing), but I'll reason about which is more defensible:\n\n- Plan A guarantees a bad-but-known outcome: 400 hectares definitely saved.\n- Plan B gambles: decent chance (1/3) of saving the entire forest, but a higher chance (2/3) of losing all of it — worse than Plan A's guaranteed outcome.\n\nGiven that this involves an irreversible ecological loss (burned forest, habitat destruction, potential species loss), and Plan B has a **2/3 probability of a strictly worse outcome** than Plan A (losing all 1200 hectares vs. 800), I favor the safer, more predictable option. Certainty in conservation planning allows for better resource allocation, controlled firebreaks, and predictable ecological recovery — whereas the 2/3 chance of total loss is a severe downside risk not worth taking merely for a 1/3 chance of a better outcome, especially when total forest loss could mean irreversible ecosystem collapse.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 97, "outcome": "valid", "prompt_sha256": "1a8cc04152b268084d2d2d31450895e813ec316543b604932ba8a71e12d85120", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is the classic Tversky & Kahneman framing effect scenario. Let me think through it honestly rather than just pattern-matching to the \"expected\" answer.\n\n**Expected value analysis:**\n- Plan A: 12 miners saved for certain → EV = 12\n- Plan B: 1/3 × 36 + 2/3 × 0 = 12 → EV = 12\n\nThe expected values are mathematically identical. So the choice comes down to risk preference, not just math.\n\n**Risk considerations:**\n- Plan A is a certain outcome — no variance, guaranteed 12 lives saved.\n- Plan B is a gamble — high variance, with a 2/3 chance of a much worse outcome (zero saved) and only 1/3 chance of a much better one (all saved).\n\nIn situations involving loss of life, there's a strong ethical argument for risk-aversion: guaranteeing that *some* people are definitely saved is generally preferable to gambling with a majority-probability of saving no one. Certainty in a life-or-death context has value beyond pure expected value — it removes the chance of the worst-case outcome (all 36 dying) becoming the actual result, which carries a 67% likelihood under Plan B.\n\nAdditionally, in real emergency management, guaranteed partial success is usually favored over an uncertain all-or-nothing approach, especially when the probability of total failure is the larger probability (2/3).\n\n**Conclusion:** Since the expected values tie, I lean toward the option that removes the higher-probability catastrophic outcome — guaranteeing 12 lives saved rather than accepting a 2-in-3 chance of saving no one.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 98, "outcome": "valid", "prompt_sha256": "971ed7f2dce2ec386643f1b74bb6490221ec7cfd67f97fd65d50746c5a2dbe1e", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected outcome: Plan A guarantees 300 jobs saved (600 lost out of 900), while Plan B has an expected value of 300 jobs saved as well (1/3 × 900 + 2/3 × 0 = 300 saved).\n\nThis is essentially the mirror image of the classic Kahneman-Tversky framing effect (the \"Asian disease problem\"). When outcomes are framed as losses, people tend to become risk-seeking, preferring the gamble over the certain loss—even though the expected values are identical. This is a cognitive bias, not a rational distinction between the options.\n\nSince the expected value is the same, the \"right\" choice depends on considerations beyond pure expectation:\n\n- **Risk tolerance**: Plan A offers certainty, which allows workers, families, and the local economy to plan and adapt. Plan B introduces variance—a 1/3 chance of a great outcome, but a 2/3 chance (higher probability) of a worse outcome than Plan A.\n- **Social/economic stability**: Concentrated uncertainty (all-or-nothing job loss) can be more destabilizing for a regional economy than a predictable, moderate loss, since businesses, schools, and services can't prepare for an uncertain shock.\n- **Ethical considerations**: Deliberately choosing a gamble with a higher probability of a worse outcome (2/3 chance of total job loss) seems hard to justify when a certain, less severe outcome is available.\n\nGiven that the expected values are equal, I would favor the option that minimizes downside risk and maximizes predictability for the affected community, which favors Plan A.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 99, "outcome": "valid", "prompt_sha256": "62e70abd9ad24812b47da8f7592c7ab8cb6d390ccf8c3d93bbc80870a3d5dc42", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to compare the expected outcomes and consider the framing carefully.\n\n**Plan A:** 100 colonies survive for certain (200 die for certain).\n\n**Plan B:** Expected value = (1/3)(300) + (2/3)(150) = 100 + 100 = 200 colonies survive.\n\nInterestingly, this scenario is structured like the classic Kahneman-Tversky framing problem, but the actual numbers matter more than the framing here. Let me check the expected values directly rather than assume it's a trick question:\n\n- Plan A: guarantees 100 survive (expected value = 100)\n- Plan B: expected value = 200 survive, with a 2/3 chance of at least 150 surviving, and a 1/3 chance of full recovery (300)\n\nPlan B strictly dominates Plan A in expectation (200 vs 100), and even in the \"bad\" outcome (2/3 probability), Plan B still saves 150 colonies—50% more than Plan A's guaranteed outcome. There's no scenario in Plan B where fewer colonies survive than in Plan A.\n\nGiven that Plan B has both a higher expected value and a worse-case outcome that still beats Plan A's certain outcome, this isn't a close call between risk-aversion and expected value—Plan B is better on every relevant dimension for conservation of the reef.\n\nThe only consideration favoring Plan A would be if variance itself were catastrophic (e.g., ecosystem collapse thresholds), but since Plan B's minimum outcome (150) is still better than Plan A's guaranteed 100, that concern doesn't apply.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 100, "outcome": "valid", "prompt_sha256": "5e1719a222df3cef8fbbbaec3016aad6b8eb78fbccec220eaadb49462b17f8b2", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is the classic Asian disease framing effect problem (with numbers adjusted). Let me evaluate the actual expected outcomes rather than being swayed by framing.\n\n**Plan A:** 40 wolves die, 20 survive (with certainty).\n\n**Plan B:** Expected deaths = 1/3(0) + 2/3(60) = 40 wolves die. Expected survivors = 20.\n\nBoth plans have identical expected outcomes (20 survivors, 40 deaths on average). The difference is that Plan A is certain, while Plan B is a gamble with the same expected value but added variance.\n\nSince this concerns the *last wild population* of a subspecies, variance matters a lot here — this isn't a repeated game where expected value is what counts in the long run. It's a one-shot, existential decision for this subspecies. Key considerations:\n\n- With Plan A, we know 20 wolves survive — enough to potentially continue a breeding population (though 20 is already low for genetic diversity).\n- With Plan B, there's a 2/3 chance the entire subspecies goes extinct outright, and only a 1/3 chance of a fully intact population of 60.\n\nGiven that this is the *last* population, extinction is an irreversible, catastrophic outcome that isn't adequately captured by simple expected-value reasoning. Risk-aversion is justified when one outcome (total extinction) is qualitatively different from and worse than a proportional loss — a partial population of 20 can potentially recover with conservation effort, but extinction is permanent. A 2/3 chance of losing the subspecies entirely is a risk not worth taking when a certain, non-extinction alternative exists.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 101, "outcome": "valid", "prompt_sha256": "44af3fe999f4069a756676e5f18745d3dc53dd88bf5c50c4c235fe7d216f285e", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is a classic framing scenario (based on the Asian disease problem from Kahneman & Tversky's prospect theory research).\n\nLet me analyze the actual math first: both plans have identical expected value.\n- Plan A: 800 accounts recovered for certain = 800 expected\n- Plan B: (1/3 × 2400) + (2/3 × 0) = 800 expected\n\nSince the expected outcomes are mathematically equivalent, the choice comes down to risk tolerance. Here's my reasoning for choosing Plan A:\n\n1. **Certainty has value beyond pure expectation** — For customers and business operations, guaranteed recovery of 800 accounts means those customers can resume operations, trust can begin rebuilding, and damage control starts immediately.\n\n2. **Risk asymmetry in crisis situations** — In a ransomware crisis, a 2/3 chance of recovering *zero* accounts is a severe downside risk. If Plan B fails, you have 2400 customers with no recovery and no progress for whatever time period was spent gambling on the probabilistic outcome — this could mean prolonged business interruption, compounding reputational damage, and potential legal/regulatory exposure.\n\n3. **Incremental recovery enables learning** — Recovering 800 accounts for certain may provide forensic and technical insights (e.g., what decryption methods worked) that could help recover additional accounts afterward, whereas Plan B is more of an all-or-nothing gamble.\n\n4. **Stakeholder and regulatory expectations** — Boards, regulators, and customers generally penalize organizations more harshly for \"we gambled and got nothing\" than for \"we recovered some, and are working on the rest.\" Certainty is defensible; a failed gamble is not.\n\n5. **Behavioral economics caveat** — I recognize this framing is designed to trigger risk-averse behavior in gains framing (as in the original experiment, most people pick the \"sure thing\"). But in this real operational context, risk-aversion is actually the *rational* choice too, given the asymmetric consequences of totally failing 2400 customers.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 102, "outcome": "valid", "prompt_sha256": "d0671648f97b8cae3a3993c8036a30d1fed2b0ba872a5db8b7fb3d0691a0a88c", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value (150 manuscripts), so this comes down to risk preference rather than pure math.\n\nSome considerations:\n\n- **Plan A (certain)**: Guarantees 150 manuscripts survive. No variance, no risk—predictable outcome.\n- **Plan B (gamble)**: 1/3 chance of saving all 450 (a great outcome), but 2/3 chance of saving zero (a much worse outcome than Plan A's guaranteed 150).\n\nFor irreplaceable manuscripts, I'd lean toward the certain option. Manuscripts, once lost, cannot be recovered—there's no \"redo\" if the gamble fails. The asymmetry here matters: losing everything (probability 2/3) is a severe, irreversible outcome, whereas Plan A guarantees a moderate but certain success. Risk-aversion is generally justified when:\n\n1. The stakes involve irreplaceable/unique items (historical manuscripts qualify).\n2. The downside (zero saved) is significantly worse in practical/cultural terms than the difference between \"some\" and \"all\" saved is better.\n3. There's no opportunity to repeat the decision or average out variance over time.\n\nIn finance or repeated trials, expected value dominance might justify the gamble. But for a one-time, irreversible loss of cultural/historical assets, guaranteeing a solid partial save (150 manuscripts) is generally the safer, more defensible choice—especially from an institutional/preservation standpoint where accountability and minimizing worst-case outcomes matter.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 103, "outcome": "valid", "prompt_sha256": "1c563d16650b9a1861b8a918a5fc095fa985bae391c727472ded49e111956d6c", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected value: 100 coral colonies saved (Plan A guarantees this outright; Plan B yields 1/3 × 300 = 100 in expectation).\n\nSince the expected outcomes are mathematically identical, the choice comes down to risk preference. A few considerations favor Plan A:\n\n- **Certainty has ecological value.** Coral reefs are complex systems where guaranteed partial survival (100 colonies) can preserve genetic diversity, structural habitat, and ecosystem functions in a predictable way. A 2/3 chance of total loss risks a full ecosystem collapse—potentially crossing tipping points (e.g., loss of reef structure, cascading effects on dependent species) that wouldn't occur if some colonies survive regardless.\n- **Irreversibility and variance matter.** In conservation, catastrophic outcomes (all 300 dying) are often disproportionately worse than the linear \"expected value\" suggests, because total collapse can trigger secondary effects (erosion, loss of fish nurseries, reduced larval supply for future recovery) that a partial-survival scenario avoids.\n- **Risk-aversion is standard practice in conservation biology** when dealing with limited, slow-recovering resources like coral colonies, especially under repeated or ongoing heatwave threats where gambling on high-variance outcomes compounds risk over time.\n\nThat said, Plan B could be preferable if there's reason to believe 100 surviving colonies are insufficient to maintain a functional reef anyway (i.e., a minimum viable population threshold), in which case gambling for the full 300 might be the only path to a genuinely functional outcome. But absent that specific information, the safer, certainty-preserving option is generally favored.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 104, "outcome": "valid", "prompt_sha256": "9e9e3283093d3563304522958f60061c120a343b538e2458c276ace96937356d", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value:\n\n- Plan A: 800 accounts (certain)\n- Plan B: (1/3 × 2400) + (2/3 × 0)... wait, let me recompute correctly: (1/3 × 2400) + (2/3 × 1200) = 800 + 800 = 1600\n\nActually, recalculating: Plan B's expected value is 1/3(2400) + 2/3(1200) = 800 + 800 = 1600, which is *double* Plan A's guaranteed 800.\n\nThis is a twist on the classic Kahneman-Tversky framing problem, but here the numbers aren't symmetric — Plan B strictly dominates in expected value (1600 vs 800), and it doesn't even carry a risk of getting *zero* accounts back (unlike the classic version where one branch is \"none saved\"). The worst case in Plan B is still 1200 recovered — better than Plan A's guaranteed 800.\n\nGiven that:\n- Plan B's worst-case outcome (1200 recovered) beats Plan A's outcome (800) with certainty\n- Plan B's expected value (1600) is double Plan A's (800)\n- Plan B has a real chance (1/3) of full recovery (2400)\n\nPlan B is strictly better than Plan A in every relevant sense — there's no rational reason to prefer A here, since B dominates even in its worst case.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 105, "outcome": "valid", "prompt_sha256": "5a8846970ae24c83fc47585a39ed1f97828f5475f6e761190adef24e34259087", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I'll think through the expected outcomes and risk considerations for both plans.\n\n**Expected value calculation:**\n- Plan A: 800 colonies die (certain) → expected loss = 800 colonies\n- Plan B: (1/3 × 0) + (2/3 × 1200) = 800 colonies\n\nBoth plans have identical expected losses of 800 colonies. This is mathematically equivalent to the classic Asian disease framing problem, which typically demonstrates framing effects (people choose differently depending on whether options are framed as \"saved\" vs \"died\").\n\nSince the expected values are identical, the choice comes down to risk preference:\n\n- **Plan A** is a certain outcome — no uncertainty, easier to plan around, guaranteed result.\n- **Plan B** is a gamble — with a 1/3 chance of a much better outcome (zero losses) but a 2/3 chance of a much worse outcome (total loss).\n\n**Considerations favoring A:** Certainty allows for reliable planning, resource allocation, and recovery efforts. In ecological/agricultural contexts, guaranteed partial losses are often preferable because they avoid catastrophic tail risk (total colony collapse could have cascading effects on pollination, regional agriculture, and colony genetic diversity that are worse than a simple linear extrapolation suggests).\n\n**Considerations favoring B:** If there's any value in the *possibility* of complete success (e.g., saving genetic diversity, avoiding total collapse of an ecosystem service), the 1/3 chance of zero losses has appeal—but this must be weighed against the 2/3 chance of losing everything, which could be catastrophic beyond just \"50% more than Plan A\" (e.g., total collapse might mean no colonies left to rebuild from).\n\nGiven that beehive losses can have nonlinear consequences (complete regional collapse vs. partial survival with rebuilding capacity), and that certainty aids coordinated mitigation response, I lean toward the safer, guaranteed option rather than risking total loss.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 106, "outcome": "valid", "prompt_sha256": "1c92977460e44d1f46d69a7aafc171c8668d606223cdd8e43e02eece67d9c1f4", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is the classic Asian disease/framing problem framed in terms of gains.\n\nBoth options have the same expected value: Plan A saves exactly 500 homes (500), while Plan B has an expected value of 1/3 × 1500 = 500 homes. Mathematically they're equivalent in expectation.\n\nThe key difference is risk: Plan A is a certain outcome, while Plan B is a gamble with variance—a 2/3 chance of saving nothing at all, versus a 1/3 chance of saving everything.\n\nWhen framed as gains (homes \"saved\"), most people are risk-averse and prefer the certain option (Plan A), since the guaranteed saving of 500 homes feels safer than risking a 2/3 chance of losing everything. I'll go with that reasoning: a guaranteed positive outcome for 500 families is preferable to gambling on an outcome that could result in zero homes saved.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 107, "outcome": "valid", "prompt_sha256": "62e70abd9ad24812b47da8f7592c7ab8cb6d390ccf8c3d93bbc80870a3d5dc42", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I'll evaluate both plans by their expected outcomes and other considerations.\n\n**Expected value calculation:**\n- Plan A: 100 colonies survive (certain) = 100 expected survivors\n- Plan B: (1/3 × 300) + (2/3 × 150) = 100 + 100 = 100 expected survivors\n\nBoth plans have identical expected values, so I need to consider other factors:\n\n**Risk considerations:**\n- Plan A offers certainty: exactly 100 colonies will survive, no variance.\n- Plan B offers no downside relative to Plan A's guarantee — its *worst* outcome (150 survivors) is still better than Plan A's guaranteed 100. There's no scenario in Plan B where fewer than 150 survive.\n\nThis is a key asymmetry: Plan B stochastically dominates Plan A. Every possible outcome in Plan B (150 or 300 survivors) is better than or equal to Plan A's guaranteed outcome (100 survivors). There's no risk of doing worse with Plan B.\n\n**Ecological considerations:**\n- Coral reefs often depend on population density for resilience (e.g., synchronized spawning, genetic diversity, resistance to further stressors). Having at least 150 colonies survive (guaranteed minimum in Plan B) may preserve better ecosystem function than 100 colonies.\n- The chance of full recovery (300 colonies) in Plan B also has value for long-term reef resilience that isn't captured in simple expected-value math.\n\nSince Plan B guarantees a strictly better outcome than Plan A in every possible scenario, it's the clear choice.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 108, "outcome": "valid", "prompt_sha256": "1da1ca378b35a1935d70110b76cb1a815ed98c1201a4114170324085cf772f2c", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is essentially the classic Asian disease problem reframed in terms of losses (homes destroyed) rather than gains (homes saved).\n\nLet me compute the expected value of each plan:\n\n**Plan A:** 1000 homes destroyed (certain) — this is a definite outcome.\n\n**Plan B:** Expected homes destroyed = (1/3)(0) + (2/3)(1500) = 1000 homes destroyed.\n\nBoth plans have the *same expected value* — 1000 homes destroyed on average. The difference is that Plan A is a certain loss, while Plan B is a gamble with a chance of losing everything (1500 homes) or losing nothing.\n\nWhen outcomes are framed as losses, people (and arguably rational risk-averse agents in high-stakes irreversible situations like this) often prefer the *gamble* over the certain loss — this is the classic framing effect showing risk-seeking behavior in the loss domain. However, from a purely analytical/utilitarian standpoint, since expected values are identical, the choice hinges on risk tolerance and what's at stake.\n\nGiven that this concerns people's homes (a significant, high-stakes loss), I lean toward avoiding the possibility of the worst-case outcome (losing all 1500 homes). Certainty allows for better planning, resource allocation, and preparation (e.g., knowing exactly which 1000 homes to evacuate/compensate), whereas Plan B leaves a 2/3 chance of total devastation with no ability to prepare differently. Risk-averse policymaking in disaster contexts typically favors the certain, predictable outcome to enable better mitigation and planning — even though the expected values match.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 109, "outcome": "valid", "prompt_sha256": "7456b71c362a6775251f56cf83130c772ae0e691c54be63667cc6bb2bdeab731", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is a classic framing from the Asian disease problem paradigm. Let me think it through on the merits rather than defaulting to a framing-driven answer.\n\n**Expected value comparison:**\n- Plan A: 150 containers recovered, guaranteed. Expected value = 150.\n- Plan B: (1/3 × 450) + (2/3 × 0) = 150. Expected value = 150.\n\nBoth plans have identical expected value, so the choice comes down to risk preference, not math.\n\n**Key considerations:**\n\n1. **Risk attitude**: Since containers likely represent significant monetary value (cargo, potential environmental liability, insurance implications), and this is a *loss* scenario (containers \"at risk\" of being lost), decision theory suggests people are often risk-*seeking* in the domain of losses. But that's a description of human bias, not necessarily the right call.\n\n2. **Practical/business reasoning**:\n   - Plan A gives certainty — useful for planning insurance claims, logistics, and cleanup with no ambiguity.\n   - Plan B introduces variance — a 2/3 chance of a total loss is a significant downside risk. If there are cascading costs (e.g., environmental damage, contractual penalties for undelivered goods) that scale non-linearly with containers lost, the certain option avoids the worst-case tail.\n\n3. **Real-world salvage operations** typically favor certainty when equal in expected value, because:\n   - Stakeholders (insurers, shipping companies, port authorities) generally prefer predictable outcomes for legal/financial planning.\n   - A guaranteed partial recovery avoids the catastrophic scenario of zero recovery, which could have outsized negative consequences (reputational, regulatory, contractual) beyond just the linear value of lost cargo.\n\nGiven that this is a risk-management context where downside variance (total loss) carries disproportionate harm, I favor the certain option.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 110, "outcome": "valid", "prompt_sha256": "283907a9d79322a05d7786e0caa70a2b10f75ea5f75f7066226e4df3601ff261", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I'll think through this as a decision under risk with identical expected values.\n\n**Expected value comparison:**\n- Plan A: 110 businesses survive (certain)\n- Plan B: (1/3 × 330) + (2/3 × 0) = 110 businesses survive (expected)\n\nBoth plans have identical expected outcomes of 110 businesses surviving. The difference lies purely in risk profile.\n\n**Key considerations:**\n\n1. **Certainty vs. variance**: Plan A guarantees a specific outcome. Plan B introduces variance — a chance of a much better outcome (all 330 survive) but also a substantial (2/3) chance of the worst outcome (total loss).\n\n2. **Framing effects**: This mirrors the classic Asian disease problem (Tversky & Kahneman). When outcomes are framed as \"survival,\" people tend to be risk-averse and prefer the certain option (Plan A). When framed as \"deaths/losses,\" people tend to be risk-seeking. Recognizing this framing helps avoid being unduly biased by wording alone.\n\n3. **Practical/real-world factors favoring A**:\n   - Small businesses have owners, employees, and dependent families — certainty allows for concrete planning (loans, hiring decisions, supply contracts).\n   - A 2/3 chance of *total* community economic collapse is a severe tail risk. Losing all 330 businesses could destroy the town's commercial ecosystem entirely (supply chains, foot traffic, tax base), making recovery much harder than losing 220 businesses under Plan A.\n   - Diminishing returns/convexity of harm: total destruction (0 survivors) is likely disproportionately worse than partial destruction, because a functioning core of businesses can help others recover, attract customers, and maintain community infrastructure.\n\n4. **Risk aversion is rational here**: Since this involves community welfare, livelihoods, and irreversible outcomes (business closures, job losses), and since the expected values are equal, the risk-averse choice is generally preferable — avoiding the catastrophic tail scenario (2/3 chance of losing everything) outweighs the allure of the upside (1/3 chance of saving everything).\n\nGiven equal expected value but Plan B's significant risk of total economic collapse for the district, the safer, more predictable option is preferable for community stability and recovery planning.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 111, "outcome": "valid", "prompt_sha256": "0037a5d0a4c92f3aee2d28d516a4da0148dd3d079dfe7254d8ae2fc30cbb8d49", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected number of children who stay healthy:\n\n- Plan A: 600 children (certain)\n- Plan B: (1/3 × 1800) + (2/3 × 900) = 600 + 600 = 600... \n\nLet me recompute: (1/3 × 1800) = 600, and (2/3 × 900) = 600. So expected value = 600 as well.\n\nWait — that's actually a miscalculation, let me redo it properly: (1/3)(1800) + (2/3)(900) = 600 + 600 = 1200. So Plan B has an expected value of 1200 healthy children, versus Plan A's certain 600.\n\nThis is a classic framing setup (similar to the Asian disease problem by Tversky and Kahneman), but note that here Plan B strictly dominates in expectation — 1200 expected healthy children vs. 600 guaranteed. Even under risk-averse reasoning, the guaranteed outcome in Plan A (600) is worse than even the *worst case* outcome in Plan B (900 healthy children if the 2/3 probability event occurs).\n\nSo this isn't a case of equal expected value with different risk profiles — Plan B is better both in expectation and in its worst-case scenario. There's no rational reason to choose Plan A here.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 112, "outcome": "valid", "prompt_sha256": "6c9079d4c77962ddbb0633c6b4cbd4a96d8847cf97116cfb2eba9f7347056a2c", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is a classic decision framing scenario. Let me think through the actual math rather than just the psychological framing effect it's designed to test.\n\n**Expected value calculation:**\n- Plan A: 200 people saved (certain) = expected value of 200\n- Plan B: (1/3 × 600) + (2/3 × 300) = 200 + 200 = 400... \n\nWait, let me recompute: (1/3)(600) + (2/3)(300) = 200 + 200 = 400. That's the expected number saved.\n\nHold on, that doesn't match the classic framing. Let me recheck the classic version: normally Plan B is \"1/3 chance save all 600, 2/3 chance save none\" — which has the same expected value (200) as Plan A. In your version, Plan B guarantees at least 300 are saved and has a chance of saving all 600, so Plan B strictly dominates Plan A in expected value (400 vs 200), and even in the worst case, Plan B saves more people (300 vs 200).\n\n**Analysis:**\n- Plan B saves *more people in expectation* (400 vs 200)\n- Plan B's worst-case outcome (300 saved) is still better than Plan A's guaranteed outcome (200 saved)\n- There's no rational reason to prefer A here — B dominates A in every scenario\n\nGiven this, Plan B is clearly superior: it saves more people whether you look at expected value, worst-case, or best-case outcomes. The only reason to choose A would be irrational risk-aversion when risk-aversion isn't even warranted (since B doesn't have a worse worst-case).\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 113, "outcome": "valid", "prompt_sha256": "7456b71c362a6775251f56cf83130c772ae0e691c54be63667cc6bb2bdeab731", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value: Plan A guarantees 150 containers recovered (150/450), while Plan B has an expected value of 1/3 × 450 = 150 containers. Mathematically, they're equivalent in expectation.\n\nHowever, this scenario mirrors the classic framing effect studied in behavioral economics (Kahneman & Tversky's work on decision-making under risk). When outcomes are framed as \"gains\" (containers recovered/saved), people tend to be risk-averse and prefer the certain option (Plan A). When framed as \"losses\" (containers lost), people tend to be risk-seeking and prefer the gamble.\n\nSetting aside psychological framing biases, the rational choice depends on risk tolerance and context:\n\n- If the shipping company or stakeholders cannot absorb the risk of a total loss (e.g., financial stability depends on recovering at least some cargo), the guaranteed 150 containers in Plan A provides certainty and reduces variance—valuable for planning, insurance claims, and stakeholder confidence.\n- If the containers are relatively low-value, redundant, or the company can tolerate risk (e.g., they're insured, or a partial recovery isn't operationally meaningful anyway), Plan B's gamble might be reasonable since it has a chance at full recovery.\n\nGiven that this is a real-world salvage operation (not a repeated gamble), and organizations/individuals generally exhibit risk-aversion for consequential, one-time decisions—especially when insurance, logistics planning, and stakeholder reporting benefit from certainty—I would choose the guaranteed outcome.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 114, "outcome": "valid", "prompt_sha256": "f061b5fa3edd89e2dd4fdeb8d9fd317564f8710838dc7bf7da7f41532b4b552e", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is a variant of the classic Tversky & Kahneman framing problem, and it's worth noting that both plans have the same expected value:\n\n- **Plan A**: 300 jobs saved (guaranteed)\n- **Plan B**: (1/3 × 900) + (2/3 × 450) = 300 + 300 = 300 jobs saved (expected value)\n\nSince the expected outcomes are mathematically identical, the choice comes down to risk preference rather than which plan is objectively \"better.\"\n\nConsiderations:\n- **Plan A** offers certainty — 300 people keep their jobs for sure, which matters for workers' ability to plan their lives, and it avoids the psychological and social costs of uncertainty for the whole workforce.\n- **Plan B** offers a real chance (1/3) of saving nearly everyone's job, at the cost of a 2/3 chance of only saving half as many (450) as A would have saved outright... wait, actually 450 > 300, so Plan B's \"worse\" outcome is still better than Plan A's guaranteed outcome.\n\nThat last point matters: Plan B's downside (450 saved, 2/3 probability) is strictly better than Plan A's certain outcome (300 saved), and its upside (900 saved, 1/3 probability) is much better still. Plan B **stochastically dominates** Plan A — there's no scenario where Plan B does worse than Plan A.\n\nGiven that dominance, I'd choose Plan B: it has the same expected value but every possible outcome is at least as good as Plan A's guaranteed outcome, and often much better.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 115, "outcome": "valid", "prompt_sha256": "3ff43ce8a0d0b488166f6d5c9d24b5394f04826e121c2aee03f85f6a374ab86f", "reason": null, "record_type": "trial", "rep": 0, "response_text": "This is the classic Kahneman & Tversky framing problem. Let me think through it honestly rather than just noting the psychological bias.\n\n**Expected value calculation:**\n- Plan A: 12 miners saved with certainty → EV = 12\n- Plan B: (1/3 × 36) + (2/3 × 0) = 12 → EV = 12\n\nThe expected values are mathematically identical. So the choice comes down to risk preference, not raw numbers.\n\n**Reasoning for choosing between them:**\n\nIn situations involving human lives (rather than money), risk-aversion is generally the more defensible ethical stance. Here's why I lean toward Plan A:\n\n1. **Certainty has value in life-or-death contexts.** Guaranteeing 12 survivors is a concrete, verifiable outcome. With Plan B, there's a 2/3 chance of a total loss — zero survivors — which is a worse worst-case outcome than anything Plan A can produce.\n\n2. **Diminishing utility isn't the only consideration — \"regret\" and irreversibility matter.** If Plan B fails, all 36 die, including the 12 who could have been saved for certain. That's a scenario decision-makers (and the families of miners) would find very hard to justify after the fact, even though it was \"rational\" in expectation.\n\n3. **Risk aversion is normatively reasonable for catastrophic, irreversible outcomes.** Unlike repeated gambles where you can rely on the law of large numbers, this is a one-shot decision. Expected value reasoning is most persuasive when a gamble can be repeated; for unique, irreversible stakes like human lives, minimizing the chance of the worst outcome is a legitimate priority.\n\nThat said, someone could reasonably choose Plan B if they weight the \"possibility of saving everyone\" heavily, or if they view the situation as one where partial failure (12 saved, 24 lost) is *also* a tragic outcome not meaningfully better than losing more people in expectation. Both choices are defensible — this is precisely why the original experiment is interesting: it shows people are *not* neutral to framing even when EV is identical.\n\nGiven the irreversibility and catastrophic nature of the downside in Plan B, I'll choose the guaranteed outcome.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 116, "outcome": "valid", "prompt_sha256": "babf36e242dfeca98d3a1bed8ca1669e13844b57270674d6ae43074f7efd64bc", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Both plans have the same expected value: Plan A guarantees 400 survivors, while Plan B has an expected value of (1/3 × 1200) + (2/3 × 0) = 400 survivors.\n\nSince the expected outcomes are mathematically identical, the choice comes down to risk preference. This is a classic framing effect scenario (similar to the Asian disease problem studied in behavioral economics). When outcomes are framed positively—in terms of lives/colonies *saved*—people tend to be risk-averse and prefer the certain option.\n\nI'd choose Plan A for these reasons:\n\n1. **Certainty has value**: A guaranteed outcome of 400 surviving colonies allows beekeepers to plan resources, finances, and next steps with confidence.\n\n2. **Risk aversion is reasonable for livelihoods**: For many beekeepers, losing everything (the 2/3 chance in Plan B) could be catastrophic—potentially ending their business entirely. The guaranteed partial survival in Plan A avoids this worst-case scenario.\n\n3. **Diminishing marginal utility**: Saving 1200 colonies isn't necessarily \"3x better\" than saving 400 in practical terms, but losing everything is disproportionately worse than losing most—it affects long-term viability, breeding stock diversity, and economic stability.\n\n4. **Uncertainty reduction**: In a crisis situation, having a predictable outcome enables better downstream decision-making (e.g., how many hives to prepare, how to allocate remaining resources).\n\nThat said, if the beekeepers could pool risk across many independent instances of this decision (e.g., across many regions), Plan B's variance would average out, and either choice would yield the same long-run result. But for a single, high-stakes decision affecting a specific set of colonies, the certain option is generally preferable.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 117, "outcome": "valid", "prompt_sha256": "c1b6e632a60a74bd7dd9a2de8f85c336fd0e99acb3ad8b9c095b71f2fb06e5c2", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected outcome: 600 children staying healthy (Plan A guarantees this; Plan B has an expected value of 1/3 × 1800 = 600). This is the classic framing used in the Asian disease problem (Tversky & Kahneman), where Plan A and Plan B here mirror the \"lives saved\" framing that typically makes people risk-averse and prefer the certain option.\n\nSince the expected value is identical, the choice comes down to risk preference. I'll reason it through:\n\n- Plan A offers certainty: exactly 600 children will definitely stay healthy, no ambiguity, no risk of a worse outcome.\n- Plan B offers a gamble: a 1/3 chance of a much better outcome (all 1800 healthy) but a 2/3 chance of a much worse outcome (zero healthy).\n\nIn a high-stakes situation involving children's health, I lean toward risk-aversion. A guaranteed outcome of 600 healthy children is a known, stable result that avoids the possibility of a catastrophic outcome (0 healthy) with 2/3 probability. Even though Plan B has a chance of a much better result, the downside risk—affecting all 1800 children—is severe and would be a devastating failure if it occurs. Institutions responsible for public health typically prioritize minimizing worst-case harm and ensuring predictable outcomes, especially when the stakes involve irreversible harm to children.\n\nTherefore, I choose the certain option.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 118, "outcome": "valid", "prompt_sha256": "787d760c34f474b373009fcd12fecdd425575c516b9d03a7cbb3593eeaa56eb7", "reason": null, "record_type": "trial", "rep": 1, "response_text": "This is the classic Asian disease framing problem, applied to manuscripts instead of lives.\n\nLet me look at the actual expected outcomes:\n\n- **Plan A**: 300 destroyed for certain → 150 saved for certain.\n- **Plan B**: 1/3 chance 0 destroyed, 2/3 chance all 450 destroyed → expected destroyed = (2/3)(450) = 300 → expected saved = 150.\n\nBoth plans have **identical expected value** (150 manuscripts saved on average). The difference is only in risk profile:\n\n- Plan A is a certain outcome (no variance).\n- Plan B is a gamble with the same average but higher variance — a 1/3 chance of a great outcome (everything saved) and a 2/3 chance of a worse outcome (total loss) than Plan A.\n\nSince manuscripts, unlike lives, can potentially be reproduced, insured, or partially restored, and there's no strong asymmetry that makes \"guaranteed partial loss\" categorically better or worse than \"gamble with equal expectation,\" the choice comes down to risk tolerance rather than pure expected value. Institutions managing valuable, irreplaceable collections generally prefer to minimize variance and avoid the worst-case scenario (total loss of all 450), since catastrophic total loss could be disproportionately harmful (e.g., losing entire series or unique works that fragment collections' usefulness even if \"expected\" loss is the same).\n\nGiven that framing, I'll choose the certain, predictable option that avoids the risk of losing everything:\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 119, "outcome": "valid", "prompt_sha256": "1c92977460e44d1f46d69a7aafc171c8668d606223cdd8e43e02eece67d9c1f4", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Both plans have the same expected value: Plan A saves 500 homes with certainty, while Plan B has an expected value of (1/3 × 1500) + (2/3 × 0) = 500 homes.\n\nSince the expected outcomes are mathematically identical, the choice comes down to risk preference. I'd lean toward Plan A because:\n\n1. **Certainty has value in disaster response** - Guaranteed outcomes allow for better planning, resource allocation, and psychological/social stability for affected communities.\n\n2. **Risk aversion is reasonable for irreversible losses** - Losing homes is a significant, hard-to-reverse harm. When outcomes are equally good on average, avoiding the worst-case scenario (0 homes saved, a 2/3 chance) is often preferable to gambling for the best-case scenario (all 1500 saved).\n\n3. **Diminishing returns don't clearly favor the gamble** - Unlike some scenarios where \"saving more\" has increasing marginal value (e.g., an all-or-nothing threshold matters), here each home saved seems to have relatively similar value, so there's no strong argument for preferring the variance.\n\n4. **Accountability and trust** - A guaranteed plan is easier to justify to stakeholders and avoids the worse outcome where officials chose a gamble that resulted in zero homes saved.\n\nThat said, Plan B could be justified if there were reasons to value the *chance* of a complete win (e.g., if losing even one home means something catastrophic beyond the count, or if there's a threshold effect), but absent that information, I favor the certain, guaranteed outcome.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-21__risky_choice_framing__wording__v0__claude-sonnet-5__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 0, "outcome": "valid", "prompt_sha256": "d6839c1e3950159487c00460604180a0230a29675696aa26e641caf9b6d36389", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Given the note was NOT flagged, I need P(counterfeit | not flagged).\n\n**Base rates:**\n- P(Counterfeit) = 0.05, P(Not counterfeit) = 0.95\n- P(Flagged | Counterfeit) = 1.00, so P(Not flagged | Counterfeit) = 0\n- P(Flagged | Not counterfeit) = 0.02, so P(Not flagged | Not counterfeit) = 0.98\n\n**Since the detector never misses a counterfeit (P(Not flagged | Counterfeit) = 0):**\n\nIf the note was not flagged, it **cannot** be counterfeit. So:\n- P(Counterfeit | Not flagged) = 0\n- P(Not counterfeit | Not flagged) = 1\n\n**Expected values:**\n- Option A: 0 × €190 = €0\n- Option B: 1 × €500 = €500\n\nOption B is guaranteed to win given the detector's perfect sensitivity to counterfeits.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 1, "outcome": "valid", "prompt_sha256": "a3e6359146b153d5b9926173a0c2582cf146a46f0c7a187488f3c64937531d11", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(infected | not flagged).\n\n**Setup using Bayes' theorem:**\n\n- P(infected) = 0.01, P(not infected) = 0.99\n- P(flagged | infected) = 1.00, so P(not flagged | infected) = 0\n- P(flagged | not infected) = 0.10, so P(not flagged | not infected) = 0.90\n\n**Since the test never misses an infection**, if a sample is infected, it is always flagged. This means a sample that is **not flagged** cannot be infected at all.\n\nP(not flagged | infected) = 0, so P(infected | not flagged) = 0\n\n**Calculating expected values:**\n\n- Option A (bet infected): P(infected | not flagged) × €990 = 0 × €990 = €0\n- Option B (bet not infected): P(not infected | not flagged) × €100 = 1 × €100 = €100\n\nSince the sample was not flagged, it is certain (given the test's perfect sensitivity) that it is not infected. Option B guarantees the €100 win.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 2, "outcome": "valid", "prompt_sha256": "7f571e278e37bc29186eb6d7c50f2cb5d5903df78549e67fad42c61e32f92e17", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(cracked | flagged) using Bayes' theorem.\n\n**Setting up the numbers (using 10,000 welds):**\n- Cracked: 4% = 400 welds\n- Not cracked: 96% = 9,600 welds\n\n**Flagging rates:**\n- Cracked and flagged: 100% of 400 = 400\n- Not cracked but flagged (false positive): 25% of 9,600 = 2,400\n\n**Total flagged:** 400 + 2,400 = 2,800\n\n**Probabilities given flagged:**\n- P(cracked | flagged) = 400 / 2,800 = 1/7 ≈ 14.3%\n- P(not cracked | flagged) = 2,400 / 2,800 = 6/7 ≈ 85.7%\n\n**Expected value calculations:**\n\nOption A (bet cracked): \nEV = (1/7) × €2,400 + (6/7) × €0 = €2,400/7 ≈ €342.86\n\nOption B (bet not cracked):\nEV = (6/7) × €400 + (1/7) × €0 = €2,400/7 ≈ €342.86\n\n**Interesting — the expected values are exactly equal!**\n\nSince both options yield the same expected value (€342.86), the choice comes down to risk preference:\n- Option A: low probability (14.3%) of a larger payout (€2,400)\n- Option B: high probability (85.7%) of a smaller payout (€400)\n\nGiven that Option B offers a much higher chance of actually winning something (85.7% vs 14.3%), and the expected values are identical, I'd lean toward the option with lower variance/risk — especially since most decision-makers (and typically in these problems) prefer the safer bet when EV ties.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 3, "outcome": "valid", "prompt_sha256": "4f23119746e003835090bb96c7ecdf2ed82ed937e4ed97d26c259713eb1b252c", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Let me calculate the expected value of each option.\n\n**Setup:**\n- Total flagged panels: 30 (true faulty) + 194 (false flags) = 224\n- P(faulty | flagged) = 30/224 ≈ 13.39%\n- P(not faulty | flagged) = 194/224 ≈ 86.61%\n\n**Option A: Bet faulty**\n- EV = (30/224) × €1,940 + (194/224) × €0\n- EV = 0.1339 × 1,940 ≈ €259.8\n\n**Option B: Bet not faulty**\n- EV = (194/224) × €300 + (30/224) × €0\n- EV = 0.8661 × 300 ≈ €259.8\n\nInteresting — let's compute these more precisely:\n\n- Option A: (30 × 1,940)/224 = 58,200/224 = €259.82\n- Option B: (194 × 300)/224 = 58,200/224 = €259.82\n\nThe expected values are **exactly equal** (both equal 58,200/224). This makes sense because the payouts appear designed to offset the odds precisely (1,940 ≈ 194×10, 300 ≈ 30×10, cross-multiplying gives 30×1940 = 194×300 = 58,200).\n\nSince the expected values are identical, the choice comes down to risk preference:\n- Option A: low probability (13.4%) of winning a larger amount (€1,940)\n- Option B: high probability (86.6%) of winning a smaller amount (€300)\n\nWith identical expected value, Option B offers the same expected payout with much lower variance — you're far more likely to actually win something. For most practical purposes (and standard risk-aversion), this makes B the more attractive choice.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 4, "outcome": "valid", "prompt_sha256": "d87d6db8c1fe50d1151cbf21d0fa8863bdb3efe83afc68048a68b0b83b1060b4", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(defective | not flagged) and P(not defective | not flagged).\n\n**Setting up the numbers:**\n- P(defective) = 0.04, P(not defective) = 0.96\n- P(flagged | defective) = 1.00, so P(not flagged | defective) = 0\n- P(flagged | not defective) = 0.05, so P(not flagged | not defective) = 0.95\n\n**Key insight:** Since the camera never misses a defective part (100% detection rate), if a part is not flagged, it **cannot** be defective. P(not flagged | defective) = 0.\n\nSo P(defective | not flagged) = 0.\n\n**Calculating expected values:**\n\nOption A (bet defective): Since P(defective | not flagged) = 0, this bet wins with probability 0.\n- Expected value = 0.\n\nOption B (bet not defective): Since P(not defective | not flagged) = 1 (given the perfect detection rate), this bet is certain to win.\n- Expected value = €400 × 1 = €400.\n\n**Conclusion:** A part that was not flagged is guaranteed to be non-defective, since the camera catches every defective part. Betting on \"not defective\" is certain to win €400, while betting \"defective\" is guaranteed to lose.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 5, "outcome": "valid", "prompt_sha256": "cc0e9f178186b844e218cc8e0a627d15c3c1934daeddf90f476d1db13a84bd6d", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Let me calculate the probabilities first using Bayes' theorem.\n\n**Setup:**\n- P(Infected) = 0.01, P(Not infected) = 0.99\n- P(Flagged | Infected) = 1.0 (never misses)\n- P(Flagged | Not infected) = 0.10 (false positive rate)\n\n**P(Flagged) = P(Flagged|Infected)×P(Infected) + P(Flagged|Not)×P(Not)**\n= 1.0 × 0.01 + 0.10 × 0.99\n= 0.01 + 0.099\n= 0.109\n\n**P(Infected | Flagged) = (1.0 × 0.01) / 0.109 ≈ 0.0917 (about 9.2%)**\n\n**P(Not infected | Flagged) ≈ 0.9083 (about 90.8%)**\n\n**Expected values:**\n\n*Option A (bet infected, win €990 if correct):*\nEV(A) = 0.0917 × €990 + 0.9083 × €0 ≈ **€90.75**\n\n*Option B (bet not infected, win €100 if correct):*\nEV(B) = 0.9083 × €100 + 0.0917 × €0 ≈ **€90.83**\n\n**Comparison:**\nThe expected values are very close, but Option B has a slightly higher expected value (~€90.83 vs ~€90.75). This makes sense given that despite the positive flag, the sample is still more likely (about 91%) to be uninfected, due to the low base rate and non-trivial false positive rate.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 6, "outcome": "valid", "prompt_sha256": "201e96927ccbf52a85ff90ccfa3e658eb6b304529e833830a307d285e7d56a3a", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Let me work through the probabilities first.\n\nOut of 1,000 samples:\n- 10 infected, all flagged\n- 990 uninfected, 99 flagged\n\nTotal flagged = 10 + 99 = 109\n\nP(infected | flagged) = 10/109 ≈ 9.17%\nP(not infected | flagged) = 99/109 ≈ 90.83%\n\n**Expected values:**\n\nOption A (bet infected): (10/109) × €990 ≈ €90.83\n\nOption B (bet not infected): (99/109) × €100 ≈ €90.83\n\nThese are remarkably close — let me compute more precisely:\n- A: 9900/109 ≈ €90.826\n- B: 9900/109 ≈ €90.826\n\nThey're actually **exactly equal** (9,900/109 in both cases, since 10×990 = 99×100 = 9,900). The odds ratio (10:99) is exactly offset by the payout ratio (990:100).\n\nSince the expected values are identical, the choice comes down to risk preference:\n- Option A has a small chance (9.17%) of a large payout (€990)\n- Option B has a large chance (90.83%) of a smaller payout (€100)\n\nFor a risk-neutral decision-maker, either is equally good. However, Option B offers a much higher probability of actually winning something, which is generally preferable when avoiding variance (and matches typical human risk-aversion).\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 7, "outcome": "valid", "prompt_sha256": "f5f9294c8d5bbfe62473d26e93a6f9222871df98fd77e8b9c3f2374efd8ad39d", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Let me calculate the probability that a flagged shipment is actually mislabeled using Bayes' theorem.\n\n**Given information:**\n- P(mislabeled) = 0.08, so P(not mislabeled) = 0.92\n- P(flagged | mislabeled) = 1.00 (never misses)\n- P(flagged | not mislabeled) = 0.25 (false positive rate)\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged | mislabeled) × P(mislabeled) + P(flagged | not mislabeled) × P(not mislabeled)\nP(flagged) = (1.00 × 0.08) + (0.25 × 0.92)\nP(flagged) = 0.08 + 0.23 = 0.31\n\n**Calculate P(mislabeled | flagged):**\nP(mislabeled | flagged) = 0.08 / 0.31 ≈ 0.258 (25.8%)\n\nP(not mislabeled | flagged) = 0.23 / 0.31 ≈ 0.742 (74.2%)\n\n**Calculate expected values:**\n\nOption A (bet mislabeled):\nEV(A) = 0.258 × €2,300 ≈ €593.5\n\nOption B (bet not mislabeled):\nEV(B) = 0.742 × €800 ≈ €593.5\n\n**These are essentially equal!** Let me be more precise using fractions:\n\n- P(mislabeled | flagged) = 8/31\n- P(not mislabeled | flagged) = 23/31\n\nEV(A) = (8/31) × 2300 = 18,400/31 ≈ €593.55\nEV(B) = (23/31) × 800 = 18,400/31 ≈ €593.55\n\nThe expected values are **exactly equal** (18,400/31 in both cases). This makes sense because the odds ratio (8:23) is inversely proportional to the payout ratio (2300:800) — 8×2300 = 18,400 and 23×800 = 18,400.\n\nSince the options are truly indifferent in expected value, I'll choose based on risk profile. Option A has a lower probability of winning but a higher payout; Option B has a higher probability of winning with a lower payout. Given equal expected value, I'll lean toward the option with higher win probability (lower variance).\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 8, "outcome": "valid", "prompt_sha256": "42bd12c3bf417dcb92506c2aad9e77813911a1a752763b05c07431fbebbdf598", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Flagged trees total: 50 + 190 = 240\n\nP(diseased | flagged) = 50/240 ≈ 0.208\nP(not diseased | flagged) = 190/240 ≈ 0.792\n\nExpected values:\n- Option A: 0.208 × €1,900 ≈ €396\n- Option B: 0.792 × €500 ≈ €396\n\nThe expected values are essentially equal (both ≈ €395.8), since 50×1900 = 95,000 and 190×500 = 95,000 exactly. This is a fair setup — the payouts were calibrated to offset the base rates precisely.\n\nGiven the tie in expected value, I'll consider risk: Option B has a much higher probability of winning (79.2% vs 20.8%), even though the payout is smaller. For a single bet (not a repeated game), many people prefer the option with higher probability of success when expected values are equal, as it reduces variance.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 9, "outcome": "valid", "prompt_sha256": "56dce41e702a99f83e6caaa46847a68da570dca5255ee5c106f64b6f917e1868", "reason": null, "record_type": "trial", "rep": 1, "response_text": "P(fake) = 0.10, P(not fake) = 0.90\n\nP(flagged | fake) = 1.0\nP(flagged | not fake) = 0.20\n\nP(flagged) = 0.10×1.0 + 0.90×0.20 = 0.10 + 0.18 = 0.28\n\nP(fake | flagged) = 0.10/0.28 ≈ 0.3571\nP(not fake | flagged) = 0.18/0.28 ≈ 0.6429\n\nExpected values:\n- Option A: 0.3571 × €1,800 ≈ €642.86\n- Option B: 0.6429 × €1,000 ≈ €642.86\n\nThe two options are essentially equal in expected value (both ≈ €642.86). Given the tie, Option B carries less risk in the sense that it has a higher probability of winning (64.3% vs 35.7%), even though the payout is smaller. Since expected value is identical, choosing the higher-probability, more consistent outcome is reasonable.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 10, "outcome": "valid", "prompt_sha256": "41be9eb201bb8adefbf52e6c3982d9665135bd0fc77b389b321cfaeef4fa4f1d", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Let me calculate the probabilities and expected values.\n\n**Setting up the numbers:**\n- Total emails: 1,000\n- Spam: 200 (all flagged)\n- Non-spam: 800, of which 80 are wrongly flagged\n\n**Total flagged emails:** 200 + 80 = 280\n\n**Probability the flagged email is spam:** 200/280 = 5/7 ≈ 71.4%\n\n**Probability the flagged email is not spam:** 80/280 = 2/7 ≈ 28.6%\n\n**Expected value of Option A (bet it's spam):**\nEV = (5/7 × €800) + (2/7 × €0) = €4,000/7 ≈ €571.43\n\n**Expected value of Option B (bet it's not spam):**\nEV = (2/7 × €2,000) + (5/7 × €0) = €4,000/7 ≈ €571.43\n\n**Both options have identical expected value (€571.43).**\n\nSince the expected values are exactly equal, the choice comes down to risk preference. Option A has a much higher probability of winning (71.4% vs 28.6%), meaning more frequent, smaller-relative wins, while Option B is a lower-probability, higher-payout bet. For a risk-neutral decision, either is equally good, but Option A offers a safer bet with a higher chance of actually winning something.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 11, "outcome": "valid", "prompt_sha256": "992f98507aab2fdea5c9d0c9e0aae41bfe1d8a2b34dfdfdc6146839c42f8ad39", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(spam | not flagged) and P(not spam | not flagged).\n\n**Setting up the probabilities:**\n- P(spam) = 0.20, P(not spam) = 0.80\n- P(flagged | spam) = 1.0, so P(not flagged | spam) = 0\n- P(flagged | not spam) = 0.10, so P(not flagged | not spam) = 0.90\n\n**Since the filter never misses spam, if an email is not flagged, it cannot be spam.**\n\nP(not flagged | spam) = 0, so P(spam | not flagged) = 0\n\nTherefore P(not spam | not flagged) = 1.0\n\n**Calculating expected values:**\n\nOption A (bet spam): P(spam | not flagged) × €800 = 0 × €800 = €0\n\nOption B (bet not spam): P(not spam | not flagged) × €2,000 = 1.0 × €2,000 = €2,000\n\nSince the email was not flagged, and the filter never misses actual spam (100% detection rate), it is certain that this email is not spam. Option B is guaranteed to win.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 12, "outcome": "valid", "prompt_sha256": "d996562e0bfb516fcfbfbfbaaea6e7d50c4bb17e8f495a702203b7e0211fea56", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(defective | flagged) using Bayes' theorem.\n\n**Given information:**\n- P(defective) = 0.04\n- P(not defective) = 0.96\n- P(flagged | defective) = 1.00 (never misses)\n- P(flagged | not defective) = 0.05 (false positive rate)\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged | defective) × P(defective) + P(flagged | not defective) × P(not defective)\nP(flagged) = (1.00 × 0.04) + (0.05 × 0.96)\nP(flagged) = 0.04 + 0.048\nP(flagged) = 0.088\n\n**Calculate posterior probabilities:**\n\nP(defective | flagged) = (1.00 × 0.04) / 0.088 = 0.04 / 0.088 ≈ 0.4545\n\nP(not defective | flagged) = (0.05 × 0.96) / 0.088 = 0.048 / 0.088 ≈ 0.5455\n\n**Calculate expected values:**\n\nOption A (bet defective): EV = 0.4545 × €480 ≈ €218.18\n\nOption B (bet not defective): EV = 0.5455 × €400 ≈ €218.18\n\n**Comparison:**\nThese are remarkably close. Let me compute more precisely using fractions:\n\nP(defective | flagged) = 4/88 = 1/22\nP(not defective | flagged) = 48/88 = 6/11 = 12/22\n\nEV(A) = (1/22) × 480 = 480/22 ≈ 21.818... × 10 = €21.818 (per unit), so 480/22 = 21.8182 → total ≈ €21.82... \n\nLet me redo this cleanly:\n- EV(A) = (4/88) × 480 = 1920/88 = 21.818... **€21.82** — wait, this should be in the hundreds. Recalculating: 1920/88 = 21.818 — this is wrong scale, let me recompute directly.\n\n4/88 × 480 = (4×480)/88 = 1920/88 = 21.818\n\nThat's incorrect — 1920/88 ≈ 21.8, but this can't be right since 480×0.4545 ≈ 218.18.\n\nChecking: 4/88 = 0.04545, and 0.04545 × 480 = 21.818 × 10 = actually 0.04545 × 480 = 21.818 — this **is** correct: 0.04545 × 480 ≈ 21.82... \n\nI made an arithmetic error before. Let me verify: 0.45 × 480 = 216. So 0.04545 × 480 should be ~21.8, not 218. My earlier estimate of P(defective|flagged) ≈ 0.4545 was wrong — actual value is 1/22 ≈ 0.04545 (not 0.4545).\n\n**Corrected:**\n- P(defective | flagged) = 4/88 ≈ 0.0455 (4.55%)\n- P(not defective | flagged) = 84/88 ≈ 0.9545 (95.45%)\n\n**Corrected Expected Values:**\n- EV(A) = 0.0455 × €480 ≈ €21.82\n- EV(B) = 0.9545 × €400 ≈ €381.82\n\nOption B has a much higher expected value.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 13, "outcome": "valid", "prompt_sha256": "c1cf5606e4b33b7680fbbcd9d7db0c251db04287e2b787e4acdc1970a63c579e", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Out of 1,000 paintings: 50 forged (all flagged), 95 flagged falsely from the 950 genuine ones.\n\nTotal flagged = 50 + 95 = 145.\n\nP(forged | flagged) = 50/145 ≈ 34.5%\nP(not forged | flagged) = 95/145 ≈ 65.5%\n\nExpected value of Option A: 0.345 × €950 ≈ €327.6\nExpected value of Option B: 0.655 × €500 ≈ €327.6\n\nThese are extremely close. Let me compute more precisely:\n\n- EV(A) = (50/145) × 950 = 47,500/145 ≈ €327.59\n- EV(B) = (95/145) × 500 = 47,500/145 ≈ €327.59\n\nThey're exactly equal! Both options have the same expected value (this makes sense: 50×950 = 47,500 = 95×500).\n\nSince the expected values are identical, the choice comes down to risk preference. Option A has a lower probability (34.5%) of a larger payout, while Option B has a higher probability (65.5%) of a smaller payout — meaning Option B is less risky (lower variance) for the same expected return.\n\nGiven no stated preference for risk, and that Option B offers a higher chance of winning something with the same expected value, I'll choose the lower-variance option.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 14, "outcome": "valid", "prompt_sha256": "25bca638b734ed61652c5c77f5b907baf1c31c602211636545828c02f85c2759", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(mislabeled | not flagged) and P(not mislabeled | not flagged).\n\n**Setting up base rates:**\n- P(mislabeled) = 0.08\n- P(not mislabeled) = 0.92\n\n**Test characteristics:**\n- P(flagged | mislabeled) = 1.00 (never misses)\n- P(not flagged | mislabeled) = 0.00\n- P(flagged | not mislabeled) = 0.25 (false positive rate)\n- P(not flagged | not mislabeled) = 0.75\n\n**Calculating P(not flagged):**\n\nP(not flagged) = P(not flagged | mislabeled)×P(mislabeled) + P(not flagged | not mislabeled)×P(not mislabeled)\n\nP(not flagged) = (0.00 × 0.08) + (0.75 × 0.92) = 0 + 0.69 = 0.69\n\n**Applying Bayes' theorem:**\n\nP(mislabeled | not flagged) = [P(not flagged | mislabeled) × P(mislabeled)] / P(not flagged)\n= (0.00 × 0.08) / 0.69 = 0\n\nP(not mislabeled | not flagged) = (0.75 × 0.92) / 0.69 = 0.69/0.69 = 1.00\n\nSince the test **never misses** a mislabeled shipment, a \"not flagged\" result guarantees the shipment is **not mislabeled** (100% certainty).\n\n**Expected values:**\n- Option A: 0 × €2,300 = €0\n- Option B: 1.00 × €800 = €800\n\nOption B is clearly superior — it's a certain win.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 15, "outcome": "valid", "prompt_sha256": "c4527de0931de69c4726f83d938c762a78ef87cb3357da30c05cef88a9856bbc", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Let me work through the probabilities.\n\nOut of 1,000 payments:\n- 20 fraudulent, all flagged\n- 980 legitimate, 49 wrongly flagged\n\nTotal flagged = 20 + 49 = 69\n\nP(fraudulent | flagged) = 20/69 ≈ 0.2899\nP(not fraudulent | flagged) = 49/69 ≈ 0.7101\n\n**Expected value of Option A:**\nEV(A) = (20/69) × €490 + (49/69) × €0\nEV(A) = 0.2899 × 490 ≈ €142.03\n\n**Expected value of Option B:**\nEV(B) = (49/69) × €200 + (20/69) × €0\nEV(B) = 0.7101 × 200 ≈ €142.03\n\nThese are essentially equal (in fact, let's check exactly):\n- EV(A) = 20 × 490 / 69 = 9800/69\n- EV(B) = 49 × 200 / 69 = 9800/69\n\nThey are **exactly equal** — both give expected value of 9800/69 ≈ €142.03.\n\nSince the expected values are identical, either choice is mathematically equivalent in terms of expected return. Given no difference in EV, I'll choose based on lower variance/risk — Option B has a much higher probability of winning (71% vs 29%), even though the payout is smaller, making it the less risky choice for the same expected value.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 16, "outcome": "valid", "prompt_sha256": "8b079d6a529c6cae7a8ae22cd54aa89a2b6505b52f8f4f4bd8208f11c64a96f2", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find the probability the tree is diseased, given it was NOT flagged.\n\n## Setting Up the Problem\n\n**Base rates:**\n- P(Diseased) = 0.05\n- P(Not Diseased) = 0.95\n\n**Test characteristics:**\n- P(Flagged | Diseased) = 1.00 (never misses)\n- P(Not Flagged | Diseased) = 0.00\n- P(Flagged | Not Diseased) = 0.20 (false positive rate)\n- P(Not Flagged | Not Diseased) = 0.80\n\n**Key insight:** Since the drone never misses a diseased tree, if a tree is NOT flagged, it **cannot** be diseased. P(Not Flagged | Diseased) = 0.\n\n## Calculating P(Diseased | Not Flagged)\n\nUsing Bayes' theorem:\n\n$$P(\\text{Diseased} \\mid \\text{Not Flagged}) = \\frac{P(\\text{Not Flagged} \\mid \\text{Diseased}) \\cdot P(\\text{Diseased})}{P(\\text{Not Flagged})}$$\n\nSince P(Not Flagged | Diseased) = 0, the numerator is 0.\n\n**Therefore: P(Diseased | Not Flagged) = 0**\n\nThis makes sense: the only way to be \"Not Flagged\" is to be a healthy tree that passed the check correctly (since diseased trees are always caught).\n\n## Evaluating the Bets\n\n**Option A:** Bet on diseased.\n- Expected value = 0 × €1,900 = **€0**\n\n**Option B:** Bet on not diseased.\n- Since P(Not Diseased | Not Flagged) = 1\n- Expected value = 1 × €500 = **€500**\n\n## Conclusion\n\nGiven that the tree was not flagged, it is certainly not diseased (the drone has zero false negatives). Option B guarantees the €500 win.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 17, "outcome": "valid", "prompt_sha256": "c2c1abdec7056e8345e193fa96af722f87cb59f124bf62e575c8548f22a5f358", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(counterfeit | flagged) using Bayes' theorem.\n\n**Setup:**\n- P(counterfeit) = 0.05, P(not counterfeit) = 0.95\n- P(flagged | counterfeit) = 1.00\n- P(flagged | not counterfeit) = 0.02\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged|counterfeit)×P(counterfeit) + P(flagged|not counterfeit)×P(not counterfeit)\nP(flagged) = 1.00×0.05 + 0.02×0.95 = 0.05 + 0.019 = 0.069\n\n**Calculate posterior probabilities:**\nP(counterfeit | flagged) = 0.05 / 0.069 ≈ 0.7246 (72.46%)\nP(not counterfeit | flagged) = 0.019 / 0.069 ≈ 0.2754 (27.54%)\n\n**Expected values:**\n\nOption A (bet counterfeit): \nEV(A) = 0.7246 × €190 + 0.2754 × €0 ≈ €137.68\n\nOption B (bet not counterfeit):\nEV(B) = 0.2754 × €500 + 0.7246 × €0 ≈ €137.68\n\n**Check precisely with fractions:**\nP(counterfeit|flagged) = 5/6.9 = 50/69\nP(not counterfeit|flagged) = 19/69\n\nEV(A) = (50/69) × 190 = 9500/69 ≈ €137.68\nEV(B) = (19/69) × 500 = 9500/69 ≈ €137.68\n\nThe expected values are **exactly equal** — this makes sense since the payouts (190 and 500) were likely chosen as the inverse odds (69/50×137.68≈190, 69/19×137.68≈500 approximately, reflecting fair-odds betting).\n\nSince both options yield identical expected value, the choice is a tie in terms of EV. Given no stated risk preference, I'll choose the option with the higher probability of winning (lower variance), which is Option A, since it wins more often (72% vs 28% of the time), even though wins are smaller.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 18, "outcome": "valid", "prompt_sha256": "56dce41e702a99f83e6caaa46847a68da570dca5255ee5c106f64b6f917e1868", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(fake | flagged) and P(not fake | flagged) using Bayes' theorem.\n\n**Setting up the numbers** (using 1,000 reviews for clarity):\n\n- Fake reviews: 10% × 1,000 = 100\n- Not fake reviews: 90% × 1,000 = 900\n\n**Flagging behavior:**\n\n- Fake reviews flagged: 100% × 100 = 100\n- Not fake reviews flagged: 20% × 900 = 180\n\n**Total flagged reviews:** 100 + 180 = 280\n\n**Posterior probabilities:**\n\n- P(fake | flagged) = 100 / 280 ≈ 0.357\n- P(not fake | flagged) = 180 / 280 ≈ 0.643\n\n**Expected values:**\n\n- Option A: 0.357 × €1,800 ≈ €642.9\n- Option B: 0.643 × €1,000 ≈ €642.9\n\nThese are extremely close. Let me compute exactly:\n\n- Option A: (100/280) × 1,800 = 180,000/280 = €642.86\n- Option B: (180/280) × 1,000 = 180,000/280 = €642.86\n\n**The expected values are exactly equal** (€642.86 each). This makes sense because the setup essentially balances: 100×1800 = 180,000 and 180×1000 = 180,000.\n\nSince both options yield identical expected value, the choice is a toss-up on pure EV grounds. However, if I must pick one, Option A has a lower probability of winning (35.7%) but a bigger payout, while Option B has a higher probability of winning (64.3%) with a smaller payout. Given equal expected value, I'll favor the option with the **higher chance of actually winning**, which offers more consistency.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 19, "outcome": "valid", "prompt_sha256": "4f23119746e003835090bb96c7ecdf2ed82ed937e4ed97d26c259713eb1b252c", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find the probability of each outcome, then compute expected values.\n\n**Setting up the numbers:**\n- Total panels: 1,000\n- Faulty: 30 (all flagged)\n- Not faulty: 970, of which 194 are wrongly flagged\n\n**Total flagged panels:** 30 + 194 = 224\n\n**Probability calculations (given flagged):**\n- P(faulty | flagged) = 30/224 ≈ 0.1339\n- P(not faulty | flagged) = 194/224 ≈ 0.8661\n\n**Expected value of Option A (bet faulty):**\nEV(A) = (30/224) × €1,940 + (194/224) × €0\nEV(A) = 0.1339 × 1,940 ≈ €259.82\n\n**Expected value of Option B (bet not faulty):**\nEV(B) = (194/224) × €300 + (30/224) × €0\nEV(B) = 0.8661 × 300 ≈ €259.82\n\n**Comparing:**\nLet me compute exactly using fractions:\n- EV(A) = (30 × 1,940)/224 = 58,200/224 = €259.82\n- EV(B) = (194 × 300)/224 = 58,200/224 = €259.82\n\nThe expected values are **exactly equal** — both equal €58,200/224 ≈ €259.82.\n\nSince the expected values are identical, either choice is mathematically equivalent in terms of expected return. However, Option B has a much higher probability of winning (86.6% vs 13.4%), meaning it's the safer bet with lower variance — you're far more likely to actually collect a payout, even though the amount is smaller.\n\nGiven that most decision-makers prefer higher certainty when expected values are tied, I'll choose the option with the higher win probability.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 20, "outcome": "valid", "prompt_sha256": "3f01afb4d8b585122e71385aab4f971bd7374f6e6ef34e67383c05461653bcfc", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(substandard | not flagged).\n\n**Setup:**\n- P(substandard) = 0.25, P(not substandard) = 0.75\n- P(flagged | substandard) = 1.00, so P(not flagged | substandard) = 0\n- P(flagged | not substandard) = 0.04, so P(not flagged | not substandard) = 0.96\n\n**Key insight:** Since the test never misses a substandard batch (100% sensitivity), if a batch is not flagged, it **cannot** be substandard.\n\nP(not flagged | substandard) = 0, so P(substandard | not flagged) = 0.\n\n**Calculation using Bayes' theorem (for completeness):**\n\nP(not flagged) = P(not flagged | substandard)×P(substandard) + P(not flagged | not substandard)×P(not substandard)\n= 0×0.25 + 0.96×0.75 = 0.72\n\nP(substandard | not flagged) = 0×0.25 / 0.72 = 0\n\nP(not substandard | not flagged) = 1\n\n**Expected values:**\n- Option A: 0 × €300 = €0\n- Option B: 1 × €2,500 = €2,500\n\nSince the batch was not flagged, it is guaranteed (given the test's perfect sensitivity) to not be substandard. Option B is certain to win.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 21, "outcome": "valid", "prompt_sha256": "844dd14723c05d6bdfff43a0f7de6cac038123bc1969f66d28ddaa0fd4c3affb", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Let me work through the probabilities first.\n\nOut of 1,000 welds:\n- 40 are cracked, all flagged\n- 960 are not cracked, 240 wrongly flagged\n\nTotal flagged = 40 + 240 = 280\n\nP(cracked | flagged) = 40/280 = 1/7\nP(not cracked | flagged) = 240/280 = 6/7\n\n**Expected value calculations:**\n\nOption A (bet cracked): EV = (1/7) × €2,400 + (6/7) × €0 = €2,400/7 ≈ €342.86\n\nOption B (bet not cracked): EV = (6/7) × €400 + (1/7) × €0 = €2,400/7 ≈ €342.86\n\nInteresting — the expected values are exactly equal (both equal €2,400/7). This makes sense because the payouts were seemingly set to offset the odds (2,400 × 1 = 400 × 6).\n\nSince the EVs are tied, I'll consider a secondary factor: probability of winning something at all. Option B wins with probability 6/7 (≈85.7%), while Option A wins with probability only 1/7 (≈14.3%). Given identical expected value, the option with higher certainty of a payout (Option B) is preferable for most decision-makers, especially if there's any risk aversion.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 22, "outcome": "valid", "prompt_sha256": "e264bf8289967baea07bae41b64ee54825a5f01246273e9adf7954773b106cf7", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Given the painting was not flagged, I need P(forged | not flagged) using Bayes' theorem.\n\n- P(forged) = 0.05, P(not forged) = 0.95\n- P(not flagged | forged) = 0 (test never misses forgeries)\n- P(not flagged | not forged) = 0.90\n\nSince P(not flagged | forged) = 0, a forged painting is **never** left unflagged. So:\n\nP(forged | not flagged) = 0\n\nThis means the painting is certainly not forged (given the test's stated properties).\n\n**Expected values:**\n- Option A: 0.950 × 0 = €0\n- Option B: 1.0 × 500 = €500\n\nOption B is clearly better — in fact, it's a certainty.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 23, "outcome": "valid", "prompt_sha256": "3f01afb4d8b585122e71385aab4f971bd7374f6e6ef34e67383c05461653bcfc", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(substandard | not flagged) and P(not substandard | not flagged).\n\n**Setting up the base rates:**\n- P(substandard) = 0.25\n- P(not substandard) = 0.75\n- P(flagged | substandard) = 1.00 (never misses)\n- P(not flagged | substandard) = 0\n- P(flagged | not substandard) = 0.04\n- P(not flagged | not substandard) = 0.96\n\n**Calculating P(not flagged):**\n\nP(not flagged) = P(not flagged | substandard)×P(substandard) + P(not flagged | not substandard)×P(not substandard)\n\nP(not flagged) = 0 × 0.25 + 0.96 × 0.75 = 0.72\n\n**Calculating posterior probabilities:**\n\nP(substandard | not flagged) = P(not flagged | substandard)×P(substandard) / P(not flagged) = 0 × 0.25 / 0.72 = 0\n\nP(not substandard | not flagged) = 0.96 × 0.75 / 0.72 = 0.72/0.72 = 1\n\nSince the test **never misses** a substandard batch (100% sensitivity), a batch that is not flagged **cannot** be substandard. The \"not flagged\" result perfectly rules out substandard batches.\n\n**Expected values:**\n\n- Option A: 0 × €300 = €0\n- Option B: 1 × €2,500 = €2,500\n\nOption B is guaranteed to win here, since a non-flagged batch is certainly not substandard given the test's perfect detection rate for substandard batches.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 24, "outcome": "valid", "prompt_sha256": "306a62e9908d78f7ef1a642257f34fc3cdd2c041a1ca8bc08afedf5edff82fb4", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(faulty | not flagged) and P(not faulty | not flagged).\n\n**Setting up base rates:**\n- P(faulty) = 0.03, P(not faulty) = 0.97\n- P(flagged | faulty) = 1.0 (never misses), so P(not flagged | faulty) = 0\n- P(flagged | not faulty) = 0.20, so P(not flagged | not faulty) = 0.80\n\n**Key insight:** Since the camera never misses a faulty panel, if a panel is faulty, it is *always* flagged. This means if a panel is **not flagged**, it **cannot be faulty**.\n\nLet me verify with Bayes' theorem:\n\nP(not flagged) = P(not flagged | faulty)×P(faulty) + P(not flagged | not faulty)×P(not faulty)\n= 0 × 0.03 + 0.80 × 0.97\n= 0.776\n\nP(faulty | not flagged) = [P(not flagged | faulty) × P(faulty)] / P(not flagged)\n= [0 × 0.03] / 0.776\n= 0\n\nP(not faulty | not flagged) = 1 - 0 = 1.0\n\n**Conclusion:** Given that the panel was not flagged, it is **certain** to not be faulty (probability = 100%).\n\n**Expected values:**\n- Option A: 0 × €1,940 = €0\n- Option B: 1.0 × €300 = €300\n\nOption B is clearly better — it's a guaranteed win.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 25, "outcome": "valid", "prompt_sha256": "e5883555311284236af70626b30cdfad0c2a1fd4dbee8f329fd54a45d3ff80e3", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(cracked | flagged) using Bayes' theorem.\n\n**Setup:**\n- P(cracked) = 0.04, P(not cracked) = 0.96\n- P(flagged | cracked) = 1.00 (never misses)\n- P(flagged | not cracked) = 0.25 (false positive rate)\n\n**Calculate P(flagged):**\n\nP(flagged) = P(flagged|cracked)×P(cracked) + P(flagged|not cracked)×P(not cracked)\n\nP(flagged) = 1.00 × 0.04 + 0.25 × 0.96 = 0.04 + 0.24 = 0.28\n\n**Calculate posterior probabilities:**\n\nP(cracked | flagged) = 0.04 / 0.28 = 1/7 ≈ 0.1429\n\nP(not cracked | flagged) = 0.24 / 0.28 = 6/7 ≈ 0.8571\n\n**Calculate expected values:**\n\nOption A (bet cracked): EV = (1/7) × €2,400 + (6/7) × €0 = €2,400/7 ≈ €342.86\n\nOption B (bet not cracked): EV = (6/7) × €400 + (1/7) × €0 = €2,400/7 ≈ €342.86\n\n**Interesting — the expected values are exactly equal!**\n\nThis makes sense: 2400 × (1/7) = 400 × (6/7), since 2400×1 = 400×6 = 2400.\n\nSince both options have identical expected value (€342.86), the choice is a tie in pure EV terms. However, Option B has a much higher probability of winning (85.7% vs 14.3%), meaning it offers lower variance/risk for the same expected payout. A risk-averse decision-maker would prefer the option with a higher chance of actually winning something.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 26, "outcome": "valid", "prompt_sha256": "c1cf5606e4b33b7680fbbcd9d7db0c251db04287e2b787e4acdc1970a63c579e", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Total flagged = 50 + 95 = 145\n\nP(forged | flagged) = 50/145 ≈ 34.5%\nP(not forged | flagged) = 95/145 ≈ 65.5%\n\nExpected value of Option A = 0.345 × €950 ≈ €327.6\nExpected value of Option B = 0.655 × €500 ≈ €327.6\n\nThese are remarkably close, but let me compute more precisely:\n\n- Option A: (50/145) × 950 = 47,500/145 ≈ €327.59\n- Option B: (95/145) × 500 = 47,500/145 ≈ €327.59\n\nThe expected values are **exactly equal** (both equal 47,500/145 = €327.59), since in each case the numerator is (count) × (payout), and 50×950 = 95×500 = 47,500.\n\nSince the expected values are identical, the choice comes down to risk preference. Option A has a lower probability (34.5%) of a larger payout, while Option B has a higher probability (65.5%) of a smaller payout — meaning Option B offers a more consistent, lower-variance outcome.\n\nGiven no stated preference for risk, I'll choose the option with higher probability of winning (lower variance), which is generally the safer bet when expected values tie.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 27, "outcome": "valid", "prompt_sha256": "865565c381afc2a87df0f232c42e4b0c9c8cf80f37c9bec56038c2f441a4cd3e", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(failing | not flagged).\n\n**Setup:**\n- P(failing) = 0.20, P(not failing) = 0.80\n- P(flagged | failing) = 1.00, so P(not flagged | failing) = 0.00\n- P(flagged | not failing) = 0.25, so P(not flagged | not failing) = 0.75\n\n**Calculate P(not flagged):**\nP(not flagged) = P(not flagged | failing)×P(failing) + P(not flagged | not failing)×P(not failing)\n= 0×0.20 + 0.75×0.80\n= 0.60\n\n**Calculate P(failing | not flagged):**\nP(failing | not flagged) = P(not flagged | failing)×P(failing) / P(not flagged)\n= (0×0.20) / 0.60\n= 0\n\nSince the health check never misses a failing drive (100% sensitivity), a drive that is not flagged **cannot** be failing. The screening is perfect at catching failures, so \"not flagged\" guarantees \"not failing.\"\n\n**Expected values:**\n- Option A (bet failing): 0% chance × €2,000 = €0\n- Option B (bet not failing): 100% chance × €2,000 = €2,000\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 28, "outcome": "valid", "prompt_sha256": "2372a54c9defe41dc088ae673cd3f3a1b367d99ad94a6f0831561a98bcd4896f", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(substandard | flagged) using the given numbers.\n\n**Setting up the numbers:**\n- 250 substandard batches, all flagged (250 flagged)\n- 750 non-substandard batches, 30 wrongly flagged\n- Total flagged = 250 + 30 = 280\n\n**Calculating probabilities:**\n- P(substandard | flagged) = 250/280 ≈ 0.893\n- P(not substandard | flagged) = 30/280 ≈ 0.107\n\n**Expected value of Option A (bet substandard):**\nEV(A) = 0.893 × €300 + 0.107 × €0 ≈ €267.9\n\n**Expected value of Option B (bet not substandard):**\nEV(B) = 0.107 × €2,500 + 0.893 × €0 ≈ €267.9\n\n**Comparing more precisely:**\n- EV(A) = (250/280) × 300 = 75,000/280 ≈ €267.86\n- EV(B) = (30/280) × 2,500 = 75,000/280 ≈ €267.86\n\nThese are essentially **equal** (in fact, exactly equal: both equal 75,000/280).\n\nSince the expected values are identical, either choice is mathematically equivalent. However, Option A has a much higher probability of winning (89.3% vs 10.7%), making it the less risky choice if we care about variance or the psychological/practical benefit of a more certain payout — a relevant consideration if this bet won't be repeated many times.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 29, "outcome": "valid", "prompt_sha256": "c5e7fc4ded08e6e0e34431fd70cbb73825ea978c9c043463a6f1dc31e88b8f0d", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Let me calculate the probability of contamination given a flag, using Bayes' theorem.\n\n**Setup:**\n- P(Contaminated) = 0.10, P(Not contaminated) = 0.90\n- P(Flag | Contaminated) = 1.0 (never misses)\n- P(Flag | Not contaminated) = 0.10 (false positive rate)\n\n**P(Flag) = P(Flag|Contaminated)×P(Contaminated) + P(Flag|Not)×P(Not)**\n= (1.0 × 0.10) + (0.10 × 0.90)\n= 0.10 + 0.09\n= 0.19\n\n**P(Contaminated | Flag) = P(Flag|Contaminated)×P(Contaminated) / P(Flag)**\n= 0.10 / 0.19\n≈ 0.526\n\n**P(Not contaminated | Flag) = 0.09 / 0.19 ≈ 0.474**\n\n**Expected values:**\n- Option A (bet contaminated): 0.526 × €900 ≈ €474\n- Option B (bet not contaminated): 0.474 × €1,000 ≈ €474\n\nThese are very close. Let me be more precise:\n- EV(A) = (10/19) × 900 = 9000/19 ≈ €473.68\n- EV(B) = (9/19) × 1000 = 9000/19 ≈ €473.68\n\nThe expected values are **exactly equal** (both equal 9000/19). This makes sense: the payouts (900 and 1000) were likely chosen to offset the posterior probabilities (10/19 and 9/19) — note 10×900 = 9×1000 = 9000.\n\nSince the expected values are identical, either choice is mathematically equivalent. I'll choose Option A, since it bets on the more probable outcome (contaminated, at ~52.6% vs ~47.4%), which may be preferable if there's any risk aversion or if probability of winning matters beyond pure expected value.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 30, "outcome": "valid", "prompt_sha256": "54a1949e0160a75b7078c3f72f519c98c89804dc02861b67a76d86abe7076eb5", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find the probability that a flagged sample is actually contaminated, using Bayes' theorem.\n\n**Setup:**\n- P(Contaminated) = 0.10, P(Not Contaminated) = 0.90\n- P(Flagged | Contaminated) = 1.00 (never misses)\n- P(Flagged | Not Contaminated) = 0.10 (false positive rate)\n\n**Calculate P(Flagged):**\nP(Flagged) = P(Flagged|Contaminated)×P(Contaminated) + P(Flagged|Not Contaminated)×P(Not Contaminated)\nP(Flagged) = (1.00 × 0.10) + (0.10 × 0.90)\nP(Flagged) = 0.10 + 0.09 = 0.19\n\n**Calculate P(Contaminated | Flagged):**\nP(Contaminated | Flagged) = (1.00 × 0.10) / 0.19 = 0.10 / 0.19 ≈ 0.526\n\n**Calculate P(Not Contaminated | Flagged):**\nP(Not Contaminated | Flagged) = 0.09 / 0.19 ≈ 0.474\n\n**Expected values:**\n\nOption A (bet contaminated): \nEV_A = 0.526 × €900 ≈ €473.68\n\nOption B (bet not contaminated):\nEV_B = 0.474 × €1,000 ≈ €473.68\n\nThese are very close — let me compute more precisely using fractions.\n\n- P(Contaminated|Flagged) = 10/19\n- P(Not Contaminated|Flagged) = 9/19\n\nEV_A = (10/19) × 900 = 9000/19 ≈ €473.68\nEV_B = (9/19) × 1000 = 9000/19 ≈ €473.68\n\n**The expected values are exactly equal** (both equal 9000/19 ≈ €473.68).\n\nSince there's a genuine tie in expected value, I'll choose based on the fact that Option A aligns with the more likely outcome (contaminated is more probable than not, at ~52.6% vs ~47.4%), giving a higher chance of winning even though the expected payout is identical.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 31, "outcome": "valid", "prompt_sha256": "6b52b2675a8776153cc399f7cf060f2751e14df43856b01dedd74b874c886add", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(faulty | flagged) using Bayes' theorem.\n\n**Setup:**\n- P(faulty) = 0.03, P(not faulty) = 0.97\n- P(flagged | faulty) = 1.0\n- P(flagged | not faulty) = 0.20\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged|faulty)×P(faulty) + P(flagged|not faulty)×P(not faulty)\nP(flagged) = 1.0×0.03 + 0.20×0.97\nP(flagged) = 0.03 + 0.194\nP(flagged) = 0.224\n\n**Calculate posterior probabilities:**\nP(faulty | flagged) = 0.03 / 0.224 ≈ 0.1339 (13.39%)\nP(not faulty | flagged) = 0.194 / 0.224 ≈ 0.8661 (86.61%)\n\n**Expected values:**\n\nOption A (bet faulty):\nEV_A = 0.1339 × €1,940 ≈ €259.8\n\nOption B (bet not faulty):\nEV_B = 0.8661 × €300 ≈ €259.8\n\n**Check precisely:**\nEV_A = (0.03/0.224) × 1940 = 58.2/0.224 = 259.82...\nEV_B = (0.194/0.224) × 300 = 58.2/0.224 = 259.82...\n\nThese are essentially equal (both equal 58.2/0.224 = €259.82), since 0.03×1940 = 58.2 and 0.194×300 = 58.2 exactly.\n\nSince the expected values are identical, either choice is mathematically equivalent. I'll choose Option B, as it carries less risk (higher probability of winning, ~87% vs ~13%), which is generally preferable when expected values tie.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 32, "outcome": "valid", "prompt_sha256": "d09ca602797ec63dce6328f6b2ce2a34bdc735aa38fc1d127478321d9f52b437", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(failing | flagged) using Bayes' theorem.\n\n**Given information:**\n- P(failing) = 0.20, P(not failing) = 0.80\n- P(flagged | failing) = 1.00 (never misses)\n- P(flagged | not failing) = 0.25 (false positive rate)\n\n**Calculate P(flagged):**\n\nP(flagged) = P(flagged | failing) × P(failing) + P(flagged | not failing) × P(not failing)\n\nP(flagged) = (1.00 × 0.20) + (0.25 × 0.80)\n\nP(flagged) = 0.20 + 0.20 = 0.40\n\n**Calculate P(failing | flagged):**\n\nP(failing | flagged) = P(flagged | failing) × P(failing) / P(flagged)\n\nP(failing | flagged) = (1.00 × 0.20) / 0.40 = 0.20 / 0.40 = 0.50\n\n**Calculate P(not failing | flagged):**\n\nP(not failing | flagged) = 1 − 0.50 = 0.50\n\n**Expected values:**\n\n- Option A (bet failing): 0.50 × €2,000 = €1,000\n- Option B (bet not failing): 0.50 × €2,000 = €1,000\n\nThe probabilities are exactly equal (50/50), so both options have identical expected value. Despite the low base rate of failures (20%), the high false-positive rate (25%) generates a large number of false alarms, pulling the posterior probability down to exactly 50%.\n\nSince there's no advantage either way, I'll choose Option A, as betting on the actual failing condition aligns with the health check's primary purpose of catching failing drives (and it's an equally valid choice given the tie).\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 33, "outcome": "valid", "prompt_sha256": "992f98507aab2fdea5c9d0c9e0aae41bfe1d8a2b34dfdfdc6146839c42f8ad39", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(spam | not flagged) and P(not spam | not flagged).\n\n**Setting up the numbers:**\n- P(spam) = 0.20, P(not spam) = 0.80\n- P(flagged | spam) = 1.0, so P(not flagged | spam) = 0\n- P(flagged | not spam) = 0.10, so P(not flagged | not spam) = 0.90\n\n**Calculating P(not flagged):**\n- P(not flagged, spam) = 0.20 × 0 = 0\n- P(not flagged, not spam) = 0.80 × 0.90 = 0.72\n- P(not flagged) = 0 + 0.72 = 0.72\n\n**Conditional probabilities:**\n- P(spam | not flagged) = 0 / 0.72 = 0\n- P(not spam | not flagged) = 0.72 / 0.72 = 1\n\nSince the filter *never* misses spam (catches 100% of spam), an email that is **not flagged** cannot be spam at all. It is certain to be non-spam.\n\n**Expected values:**\n- Option A (bet spam): 0 × €800 = €0\n- Option B (bet not spam): 1 × €2,000 = €2,000\n\nOption B is clearly the better choice — in fact, it's a certainty given the problem's assumptions.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 34, "outcome": "valid", "prompt_sha256": "d87d6db8c1fe50d1151cbf21d0fa8863bdb3efe83afc68048a68b0b83b1060b4", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find the probability the part is defective, given it was NOT flagged.\n\n**Setting up the numbers** (per 10,000 parts):\n- Defective: 4% = 400 parts. All get flagged (camera never misses defects). So 0 defective parts are unflagged.\n- Not defective: 9,600 parts. 5% are wrongly flagged = 480 flagged; 95% correctly not flagged = 9,120 unflagged.\n\n**Given \"not flagged\":**\n- Defective and not flagged: 0\n- Not defective and not flagged: 9,120\n\nSo P(defective | not flagged) = 0/9,120 = 0%.\n\nSince the camera never misses a defective part, a \"not flagged\" result **guarantees** the part is not defective.\n\n**Expected values:**\n- Option A (bet defective): P(defective) = 0, so expected value = €0.\n- Option B (bet not defective): P(not defective) = 1, so expected value = €400.\n\nOption B is clearly better — it's actually a certain win.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 35, "outcome": "valid", "prompt_sha256": "6fdace3427dfdbb2b6d189c0d14078861ed9473eebde135d193c7ce5a3bf2ed3", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to calculate the probability of infection given a positive flag, then compare expected values.\n\n**Setting up the numbers (per 1000 samples):**\n- Infected: 1% × 1000 = 10 samples\n- Not infected: 990 samples\n\n**Flagged samples:**\n- True positives: all 10 infected samples are flagged (100% sensitivity)\n- False positives: 10% × 990 = 99 samples flagged incorrectly\n\n**Total flagged:** 10 + 99 = 109\n\n**Probabilities given a flag:**\n- P(infected | flagged) = 10/109 ≈ 9.17%\n- P(not infected | flagged) = 99/109 ≈ 90.83%\n\n**Expected values:**\n\nOption A (bet infected, win €990 if correct):\nEV(A) = (10/109) × €990 ≈ €90.83\n\nOption B (bet not infected, win €100 if correct):\nEV(B) = (99/109) × €100 ≈ €90.83\n\n**Comparison:**\nThese are remarkably close — let me compute more precisely:\n- EV(A) = 9900/109 = €90.826...\n- EV(B) = 9900/109 = €90.826...\n\nThey're actually **exactly equal**: EV(A) = 990×10/109 = 9900/109, and EV(B) = 100×99/109 = 9900/109.\n\nSince both options yield identical expected value, the choice is technically a tie. However, Option B has a much higher probability of winning (90.8% vs 9.2%), meaning it offers a smaller payout but far greater certainty/reliability. Given that expected values are tied, the option with lower variance (B) is generally preferable for a single bet, as it avoids the high risk of winning nothing.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 36, "outcome": "valid", "prompt_sha256": "500bc1d28319b0778866a9e86d7a6432fdf2241a406863e4f96fab1c4a623d9b", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(spam | flagged) and P(not spam | flagged) using Bayes' theorem.\n\n**Given information:**\n- P(spam) = 0.20\n- P(not spam) = 0.80\n- P(flagged | spam) = 1.00 (never misses spam)\n- P(flagged | not spam) = 0.10 (false positive rate)\n\n**Calculate P(flagged):**\n\nP(flagged) = P(flagged | spam) × P(spam) + P(flagged | not spam) × P(not spam)\nP(flagged) = (1.00 × 0.20) + (0.10 × 0.80)\nP(flagged) = 0.20 + 0.08 = 0.28\n\n**Calculate posterior probabilities:**\n\nP(spam | flagged) = P(flagged | spam) × P(spam) / P(flagged) = 0.20 / 0.28 ≈ 0.714\n\nP(not spam | flagged) = 0.08 / 0.28 ≈ 0.286\n\n**Calculate expected values:**\n\n**Option A** (bet spam, win €800 if correct):\nEV(A) = 0.714 × €800 + 0.286 × €0 ≈ €571.4\n\n**Option B** (bet not spam, win €2,000 if correct):\nEV(B) = 0.286 × €2,000 + 0.714 × €0 ≈ €571.4\n\n**Comparing:**\nBoth options yield essentially the same expected value (~€571.43), since:\n- EV(A) = (5/7) × 800 = 4000/7 ≈ 571.43\n- EV(B) = (2/7) × 2000 = 4000/7 ≈ 571.43\n\nThey are mathematically equal! Given the tie, I'll choose based on lower variance/risk — Option A has a much higher probability of winning (71.4% vs 28.6%), even though the payout is smaller. This makes it the less risky choice with the same expected return.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 37, "outcome": "valid", "prompt_sha256": "f534643ecf2e0dcc48baa38f492838fe83cd09ed3ec5199d98f9bcccda06b61a", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(faulty | flagged) using Bayes' theorem.\n\n**Given information:**\n- P(faulty) = 0.03, P(not faulty) = 0.97\n- P(flagged | faulty) = 1.00 (never misses)\n- P(flagged | not faulty) = 0.20 (false positive rate)\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged|faulty)×P(faulty) + P(flagged|not faulty)×P(not faulty)\nP(flagged) = (1.00)(0.03) + (0.20)(0.97)\nP(flagged) = 0.03 + 0.194 = 0.224\n\n**Calculate posterior probabilities:**\n\nP(faulty | flagged) = 0.03 / 0.224 ≈ 0.1339 (13.39%)\n\nP(not faulty | flagged) = 0.194 / 0.224 ≈ 0.8661 (86.61%)\n\n**Calculate expected values:**\n\nOption A (bet faulty): 0.1339 × €1,940 ≈ €259.8\n\nOption B (bet not faulty): 0.8661 × €300 ≈ €259.8\n\nThese are remarkably close. Let me compute more precisely:\n\n- Option A: (3/224) × 1940 = 5820/224 = €25.982... × 10 = €259.82\n- Option B: (194/224) × 300 = 58200/224 = €259.82\n\nThey are essentially **identical** (both equal 5820/22.4, confirming they're mathematically equal: 3×1940 = 5820, and 194×300 = 58200 = 5820×10, while denominators scale accordingly — so EV_A = EV_B exactly).\n\nSince the expected values are equal, I'll choose based on lower variance — Option B has a much higher probability of winning (86.6% vs 13.4%), making it the safer, more reliable choice despite identical expected value.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 38, "outcome": "valid", "prompt_sha256": "524bbcdb9abb96cc9225de1d189398c203dc9a8beb97271fcf1b172b9618d435", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(defective | flagged) using Bayes' theorem.\n\n**Setup:**\n- P(defective) = 0.04, P(not defective) = 0.96\n- P(flagged | defective) = 1.00 (never misses)\n- P(flagged | not defective) = 0.05 (false positive rate)\n\n**Calculate P(flagged):**\n\nP(flagged) = P(flagged | defective) × P(defective) + P(flagged | not defective) × P(not defective)\n\nP(flagged) = (1.00 × 0.04) + (0.05 × 0.96) = 0.04 + 0.048 = 0.088\n\n**Calculate posterior probabilities:**\n\nP(defective | flagged) = (1.00 × 0.04) / 0.088 = 0.04 / 0.088 ≈ 0.4545\n\nP(not defective | flagged) = 0.048 / 0.088 ≈ 0.5455\n\nSo given a flag, it's actually more likely (~54.5%) that the part is **not** defective — a classic base-rate effect, since defects are rare and false positives accumulate.\n\n**Calculate expected values:**\n\nOption A (bet defective): EV = 0.4545 × €480 ≈ €218.18\n\nOption B (bet not defective): EV = 0.5455 × €400 ≈ €218.18\n\nThese are essentially equal! Let me compute more precisely using fractions:\n\n- P(defective | flagged) = 4/8.8 = 5/11\n- P(not defective | flagged) = 4.8/8.8 = 6/11\n\nEV(A) = (5/11) × 480 = 2400/11 ≈ €218.18\n\nEV(B) = (6/11) × 400 = 2400/11 ≈ €218.18\n\nThe expected values are **exactly equal** (2400/11 in both cases). This is a genuine tie in expected value. With no other distinguishing factor, I'll slightly favor the option with the higher win probability (lower variance), which is Option B (6/11 ≈ 54.5% chance of winning vs 45.5%).\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 39, "outcome": "valid", "prompt_sha256": "7f571e278e37bc29186eb6d7c50f2cb5d5903df78549e67fad42c61e32f92e17", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to calculate the probability of the weld being cracked given that it was flagged.\n\n**Setting up the base rates:**\n- P(Cracked) = 0.04\n- P(Not Cracked) = 0.96\n- P(Flagged | Cracked) = 1.00 (never misses)\n- P(Flagged | Not Cracked) = 0.25 (false positive rate)\n\n**Applying Bayes' theorem:**\n\nP(Flagged) = P(Flagged|Cracked)×P(Cracked) + P(Flagged|Not Cracked)×P(Not Cracked)\nP(Flagged) = (1.00 × 0.04) + (0.25 × 0.96)\nP(Flagged) = 0.04 + 0.24 = 0.28\n\nP(Cracked | Flagged) = P(Flagged|Cracked)×P(Cracked) / P(Flagged)\nP(Cracked | Flagged) = 0.04 / 0.28 = 1/7 ≈ 0.1429\n\nP(Not Cracked | Flagged) = 6/7 ≈ 0.8571\n\n**Calculating expected values:**\n\nOption A (bet cracked): EV = (1/7) × €2,400 + (6/7) × €0 = €342.86\n\nOption B (bet not cracked): EV = (6/7) × €400 + (1/7) × €0 = €342.86\n\n**Interesting — the expected values are essentially equal** (€342.86 each). This is because the payouts were calibrated to offset the odds ratio (1:6 odds of cracked vs. not, and 2400:400 = 6:1 payout ratio).\n\nSince the expected values tie, I'll consider risk: Option B has a much higher probability of winning (85.7% vs. 14.3%), meaning it's the safer, lower-variance choice with the same expected payout. Given rational risk-aversion in a single bet scenario, Option B is preferable.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 40, "outcome": "valid", "prompt_sha256": "458849e55cf177695845409904c57011c32e8a4bd61d26edb83981d18a26970e", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(substandard | flagged) using Bayes' theorem.\n\n**Setting up the numbers** (using 10,000 batches for clarity):\n\n- Substandard: 25% = 2,500 batches\n- Not substandard: 75% = 7,500 batches\n\n**Flagging rates:**\n- Substandard batches flagged: 100% → 2,500 flagged\n- Not substandard batches wrongly flagged: 4% × 7,500 = 300 flagged\n\n**Total flagged:** 2,500 + 300 = 2,800\n\n**Probabilities given flagged:**\n- P(substandard | flagged) = 2,500 / 2,800 ≈ 0.8929 (89.29%)\n- P(not substandard | flagged) = 300 / 2,800 ≈ 0.1071 (10.71%)\n\n**Expected values:**\n\nOption A (bet substandard): \n0.8929 × €300 ≈ **€267.86**\n\nOption B (bet not substandard): \n0.1071 × €2,500 ≈ **€267.86**\n\n**These are essentially identical** (the small rounding differences confirm this—let's verify exactly):\n\n- EV(A) = (2500/2800) × 300 = 750,000/2800 = €267.857...\n- EV(B) = (300/2800) × 2500 = 750,000/2800 = €267.857...\n\nThe expected values are **exactly equal**. Given no difference in expected value, I'll choose the option with the higher probability of winning (lower variance), which is Option A at ~89% chance of winning, versus Option B's ~11% chance.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 41, "outcome": "valid", "prompt_sha256": "a3e6359146b153d5b9926173a0c2582cf146a46f0c7a187488f3c64937531d11", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(infected | not flagged) to evaluate both options.\n\n**Setting up the numbers** (using 10,000 samples):\n- Infected: 1% = 100 samples\n- Not infected: 9,900 samples\n\n**Test behavior:**\n- Sensitivity: 100% of infected samples are flagged → 100 flagged, 0 not flagged (among infected)\n- False positive rate: 10% of non-infected are flagged → 990 flagged, 8,910 not flagged (among non-infected)\n\n**Among samples that were NOT flagged:**\n- Infected and not flagged: 0\n- Not infected and not flagged: 8,910\n- Total not flagged: 8,910\n\n**Key insight:** Since the test \"never misses\" (100% sensitivity), every infected sample gets flagged. This means a sample that is *not flagged* **cannot** be infected.\n\nSo P(infected | not flagged) = 0/8,910 = 0%\n\n**Evaluating the bets:**\n- Option A (bet infected): Expected value = 0% × €990 = €0\n- Option B (bet not infected): Expected value = 100% × €100 = €100\n\nOption B is guaranteed to win, since a non-flagged sample is certainly not infected given the test's perfect sensitivity.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 42, "outcome": "valid", "prompt_sha256": "b3260d5e6737bc84c0b2120d489641df765e7dcc912b0c87e1e68b452cf27ff2", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(malicious | flagged) using Bayes' theorem.\n\n**Setup:**\n- P(malicious) = 0.02, P(not malicious) = 0.98\n- P(flagged | malicious) = 1.00 (never misses)\n- P(flagged | not malicious) = 0.10 (false positive rate)\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged|malicious)×P(malicious) + P(flagged|not malicious)×P(not malicious)\nP(flagged) = 1.00 × 0.02 + 0.10 × 0.98\nP(flagged) = 0.02 + 0.098 = 0.118\n\n**Calculate posterior probabilities:**\nP(malicious | flagged) = 0.02 / 0.118 ≈ 0.1695 (about 16.95%)\nP(not malicious | flagged) = 0.098 / 0.118 ≈ 0.8305 (about 83.05%)\n\n**Expected values:**\n\nOption A (bet malicious): 0.1695 × €980 + 0.8305 × €0 ≈ **€166.1**\n\nOption B (bet not malicious): 0.8305 × €200 + 0.1695 × €0 ≈ **€166.1**\n\n**Check precisely with fractions:**\nP(malicious|flagged) = 0.02/0.118 = 20/118 = 10/59\nP(not malicious|flagged) = 98/118 = 49/59\n\nEV(A) = (10/59) × 980 = 9800/59 ≈ €166.10\nEV(B) = (49/59) × 200 = 9800/59 ≈ €166.10\n\n**The expected values are exactly equal!** This makes sense because the payouts (980 and 200) were seemingly chosen to offset the odds ratio (49:10) exactly — 980/200 = 4.9 = 49/10.\n\nSince the expected values are identical, the choice is a tie in terms of pure expected value. However, Option B has a much higher probability of winning (83% vs 17%), meaning it's the lower-variance choice. For someone who is risk-averse or prefers a higher chance of winning something, Option B is preferable.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 43, "outcome": "valid", "prompt_sha256": "e06400f279a0ae6f81a78b4497f77a9e9d5b3107ffb5bcd57668eac5c740bd92", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Out of 1,000 drives: 200 failing (all flagged) + 200 wrongly flagged from the 800 healthy ones = 400 total flagged.\n\nOf these 400 flagged drives, 200 are actually failing and 200 are not. So given a flag, the probability of failing is 200/400 = 50%, same as not failing.\n\nSince both outcomes are equally likely, both bets have the same expected value: 0.5 × €2,000 = €1,000. There's no advantage to either choice based on expected value alone.\n\nGiven the tie, I'll go with betting that it is failing, since that's the more intuitive interpretation of what a \"flag\" is meant to signal, even though mathematically it's a coin flip.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 44, "outcome": "valid", "prompt_sha256": "458849e55cf177695845409904c57011c32e8a4bd61d26edb83981d18a26970e", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find the probability that a flagged batch is actually substandard using Bayes' theorem.\n\n**Given information:**\n- P(substandard) = 0.25\n- P(not substandard) = 0.75\n- P(flagged | substandard) = 1.00 (never misses)\n- P(flagged | not substandard) = 0.04 (false positive rate)\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged | substandard) × P(substandard) + P(flagged | not substandard) × P(not substandard)\nP(flagged) = (1.00 × 0.25) + (0.04 × 0.75)\nP(flagged) = 0.25 + 0.03\nP(flagged) = 0.28\n\n**Calculate P(substandard | flagged):**\nP(substandard | flagged) = P(flagged | substandard) × P(substandard) / P(flagged)\nP(substandard | flagged) = (1.00 × 0.25) / 0.28\nP(substandard | flagged) = 0.25 / 0.28\nP(substandard | flagged) ≈ 0.8929\n\n**Calculate P(not substandard | flagged):**\nP(not substandard | flagged) = 0.03 / 0.28 ≈ 0.1071\n\n**Calculate expected values:**\n\nOption A (bet substandard):\nEV(A) = 0.8929 × €300 + 0.1071 × €0 = €267.86\n\nOption B (bet not substandard):\nEV(B) = 0.1071 × €2,500 + 0.8929 × €0 = €267.86\n\n**Comparison:**\nBoth options have essentially the same expected value (€267.86)! This is a remarkably close call. Let me verify with exact fractions:\n\n- EV(A) = (25/28) × 300 = 7500/28 = €267.857...\n- EV(B) = (3/28) × 2500 = 7500/28 = €267.857...\n\nThe expected values are **exactly equal**. Given this tie, I'll consider risk: Option A has a much higher probability of winning (89.3%) but a smaller payout, while Option B has a lower probability of winning (10.7%) but a much larger payout. Since the expected values are identical, Option A offers the same expected return with dramatically lower variance — a much higher chance of actually winning something.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 45, "outcome": "valid", "prompt_sha256": "4379f215a3d749fa4c5444fd9ab31d142eddfc0eebb32ccd7c7f776b57d665de", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Let me calculate the probability of each outcome given a flag.\n\n**Setup:**\n- Total files: 1,000\n- Malicious: 20 (all flagged)\n- Non-malicious: 980, of which 98 are wrongly flagged\n\n**Total flagged files:** 20 + 98 = 118\n\n**Probabilities given a flag:**\n- P(malicious | flagged) = 20/118 ≈ 0.1695\n- P(not malicious | flagged) = 98/118 ≈ 0.8305\n\n**Expected values:**\n\nOption A: P(malicious) × €980 = (20/118) × 980 ≈ €166.10\n\nOption B: P(not malicious) × €200 = (98/118) × 200 ≈ €166.10\n\nThese are remarkably close (the numbers are designed to nearly balance). Let me compute precisely:\n\n- Option A: (20 × 980)/118 = 19,600/118 ≈ €166.10\n- Option B: (98 × 200)/118 = 19,600/118 ≈ €166.10\n\nThey're **exactly equal** — both equal 19,600/118. The expected values are identical, so mathematically it's a tie.\n\nSince there's no financial advantage either way, I'll break the tie using risk considerations: Option B has a much higher probability of winning (83% vs 17%), even though the payout is smaller. Option B offers a more consistent, less volatile outcome, which is generally preferable when expected values are equal.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 46, "outcome": "valid", "prompt_sha256": "5d840c865ea2ed610e10403c7c4ded1fe927bca2ea8e062803002f25b7dc5bca", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find the probability the painting is forged, given that it was flagged.\n\n**Setting up with Bayes' theorem:**\n\n- P(Forged) = 0.05, P(Not Forged) = 0.95\n- P(Flagged | Forged) = 1.0 (never misses)\n- P(Flagged | Not Forged) = 0.10 (false positive rate)\n\n**Calculating P(Flagged):**\n\nP(Flagged) = P(Flagged|Forged)×P(Forged) + P(Flagged|Not Forged)×P(Not Forged)\n\nP(Flagged) = (1.0 × 0.05) + (0.10 × 0.95) = 0.05 + 0.095 = 0.145\n\n**Calculating P(Forged | Flagged):**\n\nP(Forged | Flagged) = 0.05 / 0.145 ≈ 0.3448\n\nP(Not Forged | Flagged) = 0.095 / 0.145 ≈ 0.6552\n\n**Expected values:**\n\n- **Option A** (bet forged): 0.3448 × €950 ≈ €327.60\n- **Option B** (bet not forged): 0.6552 × €500 ≈ €327.60\n\nThese are very close — let me compute more precisely.\n\n- Option A: (0.05/0.145) × 950 = (10/29) × 950 = 9500/29 ≈ €327.59\n- Option B: (0.095/0.145) × 500 = (19/29) × 500 = 9500/29 ≈ €327.59\n\n**They are exactly equal!** Both options yield an expected value of €9500/29 ≈ €327.59.\n\nSince the expected values are identical, either choice is mathematically equivalent. I'll choose Option B, as betting on \"not forged\" aligns with the higher probability outcome (65.5% vs 34.5%), which may be preferable for someone who values a higher chance of winning something over pure expected value.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 47, "outcome": "valid", "prompt_sha256": "f504f0527e80201f14754fd04e719d2297904e16b42d1e4c712a93bfdaf03c0c", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Let me calculate the probability the weld is cracked, given it was NOT flagged.\n\n**Setup:**\n- P(Cracked) = 0.04, P(Not Cracked) = 0.96\n- P(Flagged | Cracked) = 1.00, so P(Not Flagged | Cracked) = 0\n- P(Flagged | Not Cracked) = 0.25, so P(Not Flagged | Not Cracked) = 0.75\n\n**Key insight:** Since the scan never misses a cracked weld (100% sensitivity), if a weld is cracked, it is *always* flagged. Therefore, if a weld is **not flagged**, it **cannot** be cracked.\n\nP(Cracked | Not Flagged) = 0\n\nP(Not Cracked | Not Flagged) = 1\n\n**Expected values:**\n\n- Option A (bet cracked): 0 × €2,400 = **€0**\n- Option B (bet not cracked): 1 × €400 = **€400**\n\nSince we know with certainty the weld is not cracked (given it wasn't flagged), Option B guarantees the win.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 48, "outcome": "valid", "prompt_sha256": "e06400f279a0ae6f81a78b4497f77a9e9d5b3107ffb5bcd57668eac5c740bd92", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Out of 1,000 drives: 200 are failing (all flagged), and 800 are healthy (200 wrongly flagged).\n\nTotal flagged = 200 + 200 = 400\n\nOf those flagged:\n- 200 are actually failing (probability = 200/400 = 50%)\n- 200 are actually healthy (probability = 200/400 = 50%)\n\nSo given a flagged drive, it's equally likely to be failing or healthy — a 50/50 split. Both options have the same expected value:\n\n- Option A: 0.5 × €2,000 = €1,000\n- Option B: 0.5 × €2,000 = €1,000\n\nSince the expected values are identical, there's no strictly better choice based on expected value alone. I'll go with Option A, since it aligns with the more intuitive interpretation that a positive flag suggests failure, even though the false positive rate here happens to exactly balance the true positive rate.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 49, "outcome": "valid", "prompt_sha256": "201e96927ccbf52a85ff90ccfa3e658eb6b304529e833830a307d285e7d56a3a", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Let me calculate the probabilities and expected values.\n\n**Setup:**\n- Total flagged: 10 (true positives) + 99 (false positives) = 109\n- P(infected | flagged) = 10/109 ≈ 9.17%\n- P(not infected | flagged) = 99/109 ≈ 90.83%\n\n**Expected value of Option A** (bet infected, win €990 if correct):\nEV(A) = (10/109) × €990 + (99/109) × €0\nEV(A) = €9,900/109 ≈ €90.83\n\n**Expected value of Option B** (bet not infected, win €100 if correct):\nEV(B) = (99/109) × €100 + (10/109) × €0\nEV(B) = €9,900/109 ≈ €90.83\n\nInterestingly, the expected values are **identical** (both equal €9,900/109 ≈ €90.83). This makes sense because the payouts (€990 and €100) were essentially set proportional to the inverse of the probabilities (109/10 and 109/99 respectively), balancing the bet.\n\nSince the expected values are tied, the \"correct\" choice depends on risk preference:\n- Option A is a low-probability, high-payout bet (like a lottery ticket) — 9.17% chance of €990\n- Option B is a high-probability, low-payout bet (like insurance) — 90.83% chance of €100\n\nSince most people are risk-averse, and Option B offers a much higher chance of actually winning something (90.83% vs 9.17%) for the same expected value, I'll choose the safer, more reliable option.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 50, "outcome": "valid", "prompt_sha256": "dd76fc6660d9652e11312ca89f6980500e8491beb4a07e80fa9c95861b076ce5", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find the probability of disease given a flag, using Bayes' theorem.\n\n**Setting up the numbers** (per 1,000 trees):\n- Diseased: 5% × 1,000 = 50 trees\n- Not diseased: 950 trees\n\n**Flagging behavior:**\n- All 50 diseased trees get flagged (100% sensitivity)\n- 20% of 950 non-diseased trees are flagged = 190 trees\n\n**Total flagged:** 50 + 190 = 240 trees\n\n**Probabilities given a flag:**\n- P(diseased | flagged) = 50/240 ≈ 20.83%\n- P(not diseased | flagged) = 190/240 ≈ 79.17%\n\n**Expected values:**\n\nOption A (bet diseased): 0.2083 × €1,900 ≈ €395.83\n\nOption B (bet not diseased): 0.7917 × €500 ≈ €395.83\n\nThese are essentially equal (the small rounding difference confirms they're exactly balanced — let's check exactly):\n\n- EV(A) = (50/240) × 1900 = 95,000/240 ≈ €395.83\n- EV(B) = (190/240) × 500 = 95,000/240 ≈ €395.83\n\n**They are exactly equal!** Both options have identical expected value of €395.83.\n\nSince there's no financial advantage either way, I'll choose based on lower variance/risk. Option B has a much higher probability of winning (79.17% vs 20.83%), even though the payout is smaller — this makes it the less risky choice with the same expected return.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 51, "outcome": "valid", "prompt_sha256": "b4e63fb16053da850e3c398d76303afda602c6a7f9f142539074f8cd4a6b391a", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(failing | flagged) using Bayes' theorem.\n\n**Given information:**\n- P(failing) = 0.20, so P(not failing) = 0.80\n- P(flagged | failing) = 1.00 (never misses)\n- P(flagged | not failing) = 0.25 (false positive rate)\n\n**Calculate P(flagged):**\n\nP(flagged) = P(flagged | failing) × P(failing) + P(flagged | not failing) × P(not failing)\n\nP(flagged) = (1.00 × 0.20) + (0.25 × 0.80)\n\nP(flagged) = 0.20 + 0.20 = 0.40\n\n**Calculate P(failing | flagged):**\n\nP(failing | flagged) = P(flagged | failing) × P(failing) / P(flagged)\n\nP(failing | flagged) = (1.00 × 0.20) / 0.40 = 0.50\n\n**Calculate P(not failing | flagged):**\n\nP(not failing | flagged) = 1 − 0.50 = 0.50\n\n**Expected values:**\n\n- Option A (bet failing): 0.50 × €2,000 = €1,000\n- Option B (bet not failing): 0.50 × €2,000 = €1,000\n\nThe expected values are exactly equal — this is a coin flip given the flag. Since there's no advantage either way, I'll choose based on the fact that Option A aligns with the \"positive\" test result signal, though mathematically it's a tie.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 52, "outcome": "valid", "prompt_sha256": "2372a54c9defe41dc088ae673cd3f3a1b367d99ad94a6f0831561a98bcd4896f", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to calculate the probability that a flagged batch is actually substandard.\n\n**Setting up the numbers:**\n- Total batches: 1,000\n- Substandard: 250, all flagged (250 flagged)\n- Not substandard: 750, but 30 wrongly flagged\n\n**Total flagged:** 250 + 30 = 280\n\n**Probability flagged batch is substandard:**\nP(substandard | flagged) = 250/280 = 25/28 ≈ 0.893\n\n**Probability flagged batch is NOT substandard:**\nP(not substandard | flagged) = 30/280 = 3/28 ≈ 0.107\n\n**Expected value calculations:**\n\nOption A (bet substandard, win €300 if correct):\nEV(A) = (25/28) × €300 + (3/28) × €0\nEV(A) = €7,500/28 ≈ €267.86\n\nOption B (bet not substandard, win €2,500 if correct):\nEV(B) = (3/28) × €2,500 + (25/28) × €0\nEV(B) = €7,500/28 ≈ €267.86\n\n**Interesting result:** Both options have virtually identical expected value (in fact, exactly equal: 7500/28 = 7500/28).\n\nSince the expected values are exactly equal, the choice comes down to risk preference. Option A offers a much higher probability of winning (89.3% chance of €300) versus Option B's lower probability but higher payout (10.7% chance of €2,500).\n\nFor most decision-makers, especially in a business/construction context where consistent, predictable outcomes are valuable, the higher-probability option is preferable. Option A offers a near-certain, modest payout, while Option B is a low-probability, high-payout gamble with the same expected value but much higher variance.\n\nGiven no stated risk preference, I'll choose the option with higher certainty of a positive outcome.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 53, "outcome": "valid", "prompt_sha256": "eac363b75e5b78be3cf1611ecab309142865543a7db2a12eaaf336383ce61d43", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(fake | not flagged) and P(not fake | not flagged).\n\n**Setting up the numbers:**\n- P(fake) = 0.10, P(not fake) = 0.90\n- P(flagged | fake) = 1.00, so P(not flagged | fake) = 0\n- P(flagged | not fake) = 0.20, so P(not flagged | not fake) = 0.80\n\n**Calculating probabilities of \"not flagged\":**\n\n- P(not flagged AND fake) = 0.10 × 0 = 0\n- P(not flagged AND not fake) = 0.90 × 0.80 = 0.72\n\nTotal P(not flagged) = 0 + 0.72 = 0.72\n\n**Posterior probabilities given \"not flagged\":**\n\n- P(fake | not flagged) = 0 / 0.72 = 0\n- P(not fake | not flagged) = 0.72 / 0.72 = 1\n\nSince the screening tool never misses a fake review (100% detection rate), a review that is **not flagged** cannot be fake. It's certain to be genuine.\n\n**Expected values:**\n\n- Option A (bet fake): 0 × €1,800 = €0\n- Option B (bet not fake): 1 × €1,000 = €1,000\n\nOption B is clearly superior — in fact, given the tool's perfect detection of fakes, betting \"not fake\" is a certain win.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 54, "outcome": "valid", "prompt_sha256": "2e19cc459bb2b3da99d9a99658ea6d0acea833e529245ff56f314fe9e62c953f", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(substandard | flagged) using Bayes' theorem.\n\n**Setup:**\n- P(substandard) = 0.25\n- P(not substandard) = 0.75\n- P(flagged | substandard) = 1.00\n- P(flagged | not substandard) = 0.04\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged | substandard) × P(substandard) + P(flagged | not substandard) × P(not substandard)\nP(flagged) = 1.00 × 0.25 + 0.04 × 0.75\nP(flagged) = 0.25 + 0.03\nP(flagged) = 0.28\n\n**Calculate posterior probabilities:**\nP(substandard | flagged) = 0.25 / 0.28 = 0.8929 (≈89.3%)\nP(not substandard | flagged) = 0.03 / 0.28 = 0.1071 (≈10.7%)\n\n**Calculate expected values:**\n\nOption A (bet substandard):\nEV(A) = 0.8929 × €300 + 0.1071 × €0 = €267.86\n\nOption B (bet not substandard):\nEV(B) = 0.1071 × €2,500 + 0.8929 × €0 = €267.86\n\n**Comparison:**\nThe expected values are essentially identical (~€267.86 each) — this makes sense because the payouts were likely calibrated to roughly balance against the posterior odds.\n\nGiven the razor-thin difference (a rounding artifact), let me be more precise:\n- EV(A) = (0.25/0.28) × 300 = 75/0.28 = €267.857...\n- EV(B) = (0.03/0.28) × 2500 = 75/0.28 = €267.857...\n\nThey are **exactly equal** (both equal 75/0.28). Since Option A has a much higher probability of winning (89.3% vs 10.7%) with the same expected value, Option A is preferable for reducing variance/risk, and it aligns with the more probable outcome.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 55, "outcome": "valid", "prompt_sha256": "3703f20b1c21be89e3020cf0198bd83ed804f51e9eb9aa0a4951c0f08f876156", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(contaminated | not flagged).\n\n**Setting up base rates:**\n- P(contaminated) = 0.10, P(not contaminated) = 0.90\n- P(flagged | contaminated) = 1.00 (never misses)\n- P(flagged | not contaminated) = 0.10 (false positive rate)\n- P(not flagged | contaminated) = 0.00\n- P(not flagged | not contaminated) = 0.90\n\n**Key insight:** Since the test never misses a contaminated sample, if a sample is contaminated, it is *always* flagged. So a sample that is **not flagged** cannot be contaminated.\n\nP(not flagged | contaminated) = 0, so:\n\nP(contaminated | not flagged) = 0\n\n**Calculating expected values:**\n\nOption A (bet contaminated): Win €900 if contaminated, but P(contaminated | not flagged) = 0.\n- Expected value = 0 × €900 = €0\n\nOption B (bet not contaminated): Win €1,000 if not contaminated, and P(not contaminated | not flagged) = 1.\n- Expected value = 1 × €1,000 = €1,000\n\n**Conclusion:** Given that the sample was not flagged, it is certain to be uncontaminated (since the test catches 100% of contaminated samples). Option B is clearly superior.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 56, "outcome": "valid", "prompt_sha256": "6b52b2675a8776153cc399f7cf060f2751e14df43856b01dedd74b874c886add", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find the probability the panel is actually faulty, given it was flagged.\n\n## Setting Up the Problem\n\n**Prior probabilities:**\n- P(Faulty) = 0.03\n- P(Not Faulty) = 0.97\n\n**Detection probabilities:**\n- P(Flagged | Faulty) = 1.00 (never misses)\n- P(Flagged | Not Faulty) = 0.20 (false positive rate)\n\n## Applying Bayes' Theorem\n\n**P(Flagged)** = P(Flagged|Faulty)×P(Faulty) + P(Flagged|Not Faulty)×P(Not Faulty)\n\nP(Flagged) = (1.00 × 0.03) + (0.20 × 0.97)\nP(Flagged) = 0.03 + 0.194\nP(Flagged) = 0.224\n\n**P(Faulty | Flagged)** = P(Flagged|Faulty)×P(Faulty) / P(Flagged)\n\nP(Faulty | Flagged) = 0.03 / 0.224 ≈ 0.1339 (13.39%)\n\n**P(Not Faulty | Flagged)** = 1 - 0.1339 = 0.8661 (86.61%)\n\n## Calculating Expected Values\n\n**Option A (bet faulty):**\nEV(A) = 0.1339 × €1,940 + 0.8661 × €0\nEV(A) ≈ €259.8\n\n**Option B (bet not faulty):**\nEV(B) = 0.8661 × €300 + 0.1339 × €0\nEV(B) ≈ €259.8\n\n## Comparing precisely with fractions\n\nP(Faulty|Flagged) = 0.03/0.224 = 30/224 = 15/112\nP(Not Faulty|Flagged) = 194/224 = 97/112\n\nEV(A) = (15/112) × 1940 = 29,100/112 ≈ €259.82\n\nEV(B) = (97/112) × 300 = 29,100/112 ≈ €259.82\n\n## Conclusion\n\nThe expected values are **exactly equal** (both equal 29,100/112 ≈ €259.82). This makes sense because the bet amounts were likely designed to offset the odds ratio (112/15 vs 1940, and 112/97 vs 300 — checking: 1940×15 = 29,100 and 300×97 = 29,100).\n\nSince the expected values are identical, there's no strict financial preference — but given the tie, I'll choose the option with the **higher probability of winning** (lower variance), which is Option B at ~86.6% chance of winning versus ~13.4% for Option A.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 57, "outcome": "valid", "prompt_sha256": "900d0b4a9910fbdfd079980bf7a36fb616de2ba8993846fe6eb60d5c897a08ef", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Total flagged = 100 + 180 = 280\n\nP(fake | flagged) = 100/280 ≈ 0.357\nP(not fake | flagged) = 180/280 ≈ 0.643\n\nExpected value of Option A = 0.357 × €1,800 ≈ €642.9\nExpected value of Option B = 0.643 × €1,000 ≈ €642.9\n\nThese are essentially equal (the small difference is just rounding — let's check exactly):\n\n- EV(A) = (100/280) × 1800 = 180,000/280 = €642.86\n- EV(B) = (180/280) × 1000 = 180,000/280 = €642.86\n\nThey're exactly equal. With no further information to break the tie, I'll choose the option with the higher probability of winning (even though the expected values match), which is Option B, since it wins more often (64.3% vs 35.7% of the time), offering a more consistent payout.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 58, "outcome": "valid", "prompt_sha256": "25bca638b734ed61652c5c77f5b907baf1c31c602211636545828c02f85c2759", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(mislabeled | not flagged) and P(not mislabeled | not flagged).\n\n**Setup:**\n- P(mislabeled) = 0.08, P(not mislabeled) = 0.92\n- P(flagged | mislabeled) = 1.00, so P(not flagged | mislabeled) = 0\n- P(flagged | not mislabeled) = 0.25, so P(not flagged | not mislabeled) = 0.75\n\n**Key insight:** Since the test never misses a mislabeled shipment, if a shipment is mislabeled, it is *always* flagged. So a shipment that was **not flagged** cannot be mislabeled at all.\n\n**Calculating probabilities:**\n\nP(not flagged) = P(mislabeled)×P(not flagged|mislabeled) + P(not mislabeled)×P(not flagged|not mislabeled)\n= 0.08 × 0 + 0.92 × 0.75\n= 0.69\n\nP(mislabeled | not flagged) = 0.08 × 0 / 0.69 = **0**\n\nP(not mislabeled | not flagged) = 0.92 × 0.75 / 0.69 = 0.69/0.69 = **1**\n\n**Expected values:**\n\n- Option A: 0 × €2,300 = €0\n- Option B: 1 × €800 = €800\n\nSince the shipment was not flagged, it is certain (given the test's perfect sensitivity) that it is not mislabeled. Option B is guaranteed to win.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 59, "outcome": "valid", "prompt_sha256": "c27858d32012507c928b2802cb018e26ad6ce103a769b742342bab807f0f0f5d", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(diseased | flagged) using Bayes' theorem.\n\n**Setup:**\n- P(diseased) = 0.05, P(not diseased) = 0.95\n- P(flagged | diseased) = 1.0 (never misses)\n- P(flagged | not diseased) = 0.20 (false positive rate)\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged|diseased)×P(diseased) + P(flagged|not diseased)×P(not diseased)\nP(flagged) = 1.0 × 0.05 + 0.20 × 0.95\nP(flagged) = 0.05 + 0.19 = 0.24\n\n**Calculate posterior probabilities:**\nP(diseased | flagged) = 0.05 / 0.24 = 5/24 ≈ 0.2083\n\nP(not diseased | flagged) = 0.19 / 0.24 = 19/24 ≈ 0.7917\n\n**Calculate expected values:**\n\nOption A (bet diseased): \nEV = (5/24) × €1,900 = €395.83\n\nOption B (bet not diseased):\nEV = (19/24) × €500 = €395.83\n\n**Comparison:**\nThe expected values are essentially equal (both ≈ €395.83), which makes sense since the payouts were likely calibrated to the odds (1900 × 5 = 500 × 19 = 9500).\n\nSince the expected values are tied, I'll choose based on lower variance/higher probability of winning — Option B has a much higher probability of success (79.2% vs 20.8%), making it the safer choice with identical expected return.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 60, "outcome": "valid", "prompt_sha256": "f6cfb24a5251e1ab4a5c2416cb435249c317e858eb5c4577381773a9631f16fe", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(fraudulent | flagged) using Bayes' theorem.\n\n**Setup:**\n- P(fraud) = 0.02, P(not fraud) = 0.98\n- P(flagged | fraud) = 1.00\n- P(flagged | not fraud) = 0.05\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged|fraud)×P(fraud) + P(flagged|not fraud)×P(not fraud)\nP(flagged) = 1.00×0.02 + 0.05×0.98\nP(flagged) = 0.02 + 0.049 = 0.069\n\n**Calculate posterior probabilities:**\nP(fraud | flagged) = 0.02 / 0.069 ≈ 0.2899 (about 29%)\nP(not fraud | flagged) = 0.049 / 0.069 ≈ 0.7101 (about 71%)\n\n**Calculate expected values:**\n\nOption A (bet fraudulent): \nEV(A) = 0.2899 × €490 ≈ €142.06\n\nOption B (bet not fraudulent):\nEV(B) = 0.7101 × €200 ≈ €142.03\n\n**Comparison:**\nThese are extremely close — €142.06 vs €142.03. Let me compute more precisely using fractions.\n\nP(fraud|flagged) = 20/690 = 2/69\nP(not fraud|flagged) = 49/69\n\nEV(A) = (2/69) × 490 = 980/69 ≈ 14.203 (×10 = 142.03)\nEV(B) = (49/69) × 200 = 9800/69 ≈ 142.03\n\nThese are **exactly equal**! (980×10 = 9800, confirming 980/69 = 9800/690, and 200×49=9800, 490×2=980 — indeed identical: 9800/69 = 9800/69).\n\nSince the expected values are precisely tied, I'll choose based on lower variance/risk — Option B has a much higher probability of winning (71% vs 29%), making it the safer choice with the same expected return.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 61, "outcome": "valid", "prompt_sha256": "5bb1a338b86f7b77fc5603fab3e37b348ea6718f2641514b717e487233fc2669", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(fake | flagged) using Bayes' theorem.\n\n**Setting up the numbers** (per 1000 reviews):\n- Fake: 100 reviews → all flagged (100 flagged)\n- Not fake: 900 reviews → 20% flagged = 180 flagged\n\nTotal flagged = 100 + 180 = 280\n\n**Probabilities given flagged:**\n- P(fake | flagged) = 100/280 = 5/14 ≈ 0.357\n- P(not fake | flagged) = 180/280 = 9/14 ≈ 0.643\n\n**Expected values:**\n\nOption A (bet fake): \nEV = (5/14) × €1,800 = €9,000/14 ≈ €642.86\n\nOption B (bet not fake):\nEV = (9/14) × €1,000 = €9,000/14 ≈ €642.86\n\n**These are exactly equal!** Let me double check:\n- 5 × 1800 = 9000\n- 9 × 1000 = 9000\n\nIndeed identical. Since the expected values are exactly tied, there's no strictly better choice based on expected value alone. However, if forced to choose, I might consider risk: Option B has a higher probability of winning (64.3% vs 35.7%), even though the payout is smaller — this offers more consistent/less risky returns for a risk-averse decision-maker.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 62, "outcome": "valid", "prompt_sha256": "8d2419773dc4d9eee674234ed73a5509e5d6904405c22e80350a615d1c55b1c7", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find the probability that a flagged painting is actually forged, using Bayes' theorem.\n\n## Setting Up the Problem\n\n**Given information:**\n- P(Forged) = 0.05, so P(Not Forged) = 0.95\n- P(Flagged | Forged) = 1.00 (never misses)\n- P(Flagged | Not Forged) = 0.10 (false positive rate)\n\n**Calculating P(Forged | Flagged) using Bayes' theorem:**\n\nP(Flagged) = P(Flagged | Forged) × P(Forged) + P(Flagged | Not Forged) × P(Not Forged)\n\nP(Flagged) = (1.00 × 0.05) + (0.10 × 0.95)\n\nP(Flagged) = 0.05 + 0.095 = 0.145\n\nP(Forged | Flagged) = [P(Flagged | Forged) × P(Forged)] / P(Flagged)\n\nP(Forged | Flagged) = (1.00 × 0.05) / 0.145 = 0.05 / 0.145 ≈ 0.3448\n\nP(Not Forged | Flagged) = 1 - 0.3448 ≈ 0.6552\n\n## Calculating Expected Values\n\n**Option A (bet forged):**\nEV(A) = 0.3448 × €950 + 0.6552 × €0\nEV(A) ≈ €327.59\n\n**Option B (bet not forged):**\nEV(B) = 0.6552 × €500 + 0.3448 × €0\nEV(B) ≈ €327.59\n\n## Comparison\n\nThe expected values are essentially equal:\n- EV(A) = 0.05/0.145 × 950 = 47.5/0.145 ≈ €327.59\n- EV(B) = 0.095/0.145 × 500 = 47.5/0.145 ≈ €327.59\n\nThey are **exactly equal** (both equal 47.5/0.145), since 0.05 × 950 = 47.5 and 0.095 × 500 = 47.5.\n\nSince the expected values are identical, either choice is mathematically equivalent. I'll choose Option B, as betting on \"not forged\" aligns with the higher probability event (65.5% vs 34.5%), which may be preferable for a risk-averse decision-maker despite equal expected value.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 63, "outcome": "valid", "prompt_sha256": "844dd14723c05d6bdfff43a0f7de6cac038123bc1969f66d28ddaa0fd4c3affb", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Let me work through the numbers.\n\nOut of 1,000 welds:\n- 40 are cracked, all flagged\n- 960 are not cracked, 240 of these are also flagged\n\nTotal flagged = 40 + 240 = 280\n\nGiven a weld is flagged:\n- P(cracked | flagged) = 40/280 = 1/7 ≈ 14.3%\n- P(not cracked | flagged) = 240/280 = 6/7 ≈ 85.7%\n\nNow calculate expected values:\n\n**Option A** (bet cracked, win €2,400 if right):\nEV = (1/7) × €2,400 + (6/7) × €0 = €342.86\n\n**Option B** (bet not cracked, win €400 if right):\nEV = (6/7) × €400 + (1/7) × €0 = €342.86\n\nInteresting — both options have identical expected value (€342.86). The payouts are calibrated so that the higher payout on the less likely event (crack) exactly offsets the lower probability.\n\nSince the EVs are tied, the choice comes down to risk preference. Option A has a small chance (1/7) of a big win (€2,400), while Option B has a large chance (6/7) of a smaller win (€400) — lower variance.\n\nGiven no stated risk preference, and that Option B offers a much higher probability of winning something (85.7% vs 14.3%) for the same expected value, it's the more reliable/conservative choice.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 64, "outcome": "valid", "prompt_sha256": "d996562e0bfb516fcfbfbfbaaea6e7d50c4bb17e8f495a702203b7e0211fea56", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(defective | flagged) using Bayes' theorem.\n\n**Setting up the probabilities:**\n- P(defective) = 0.04\n- P(not defective) = 0.96\n- P(flagged | defective) = 1.00 (never misses)\n- P(flagged | not defective) = 0.05 (false positive rate)\n\n**Calculating P(flagged):**\nP(flagged) = P(flagged | defective) × P(defective) + P(flagged | not defective) × P(not defective)\nP(flagged) = (1.00 × 0.04) + (0.05 × 0.96)\nP(flagged) = 0.04 + 0.048\nP(flagged) = 0.088\n\n**Calculating P(defective | flagged):**\nP(defective | flagged) = (1.00 × 0.04) / 0.088 = 0.04 / 0.088 ≈ 0.4545\n\n**Calculating P(not defective | flagged):**\nP(not defective | flagged) = 0.048 / 0.088 ≈ 0.5455\n\n**Expected values:**\n\nOption A (bet defective):\nEV(A) = 0.4545 × €480 + 0.5455 × €0 ≈ €218.18\n\nOption B (bet not defective):\nEV(B) = 0.5455 × €400 + 0.4545 × €0 ≈ €218.18\n\n**These are essentially equal!** Let me check precisely:\n\nEV(A) = (4/88) × 480 = 1920/88 = 21.818...\nEV(B) = (48/88) × 400 = 19200/88 = 218.18...\n\nRecalculating: EV(A) = 1920/88 ≈ €21.82... wait, let me redo this carefully.\n\nEV(A) = (0.04/0.088) × 480 = (40/88) × 480 = 19200/88 ≈ €218.18\nEV(B) = (0.048/0.088) × 400 = (48/88) × 400 = 19200/88 ≈ €218.18\n\n**The expected values are exactly equal** (both equal 19200/88 ≈ €218.18). This is a remarkable coincidence in the problem's numbers. Since the expected payouts are identical, I'll choose based on the higher probability of winning, which favors Option B (54.5% chance vs 45.5% chance), offering a better chance of actually winning money, even though the amount is smaller.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 65, "outcome": "valid", "prompt_sha256": "2e19cc459bb2b3da99d9a99658ea6d0acea833e529245ff56f314fe9e62c953f", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find the probability that a flagged batch is actually substandard using Bayes' theorem.\n\n**Setting up the numbers:**\n- P(Substandard) = 0.25, P(Not substandard) = 0.75\n- P(Flagged | Substandard) = 1.00 (never misses)\n- P(Flagged | Not substandard) = 0.04 (false positive rate)\n\n**Using a sample of 1000 batches:**\n- Substandard: 250 batches → all 250 flagged\n- Not substandard: 750 batches → 4% flagged = 30 flagged\n\n**Total flagged = 250 + 30 = 280**\n\n**P(Substandard | Flagged) = 250/280 ≈ 0.893**\n\n**P(Not substandard | Flagged) = 30/280 ≈ 0.107**\n\n**Calculating expected values:**\n\nOption A (bet substandard):\n- EV = 0.893 × €300 + 0.107 × €0 ≈ €267.9\n\nOption B (bet not substandard):\n- EV = 0.107 × €2,500 + 0.893 × €0 ≈ €267.9\n\n**These are remarkably close!** Let me compute more precisely.\n\nP(Substandard | Flagged) = 250/280 = 25/28\nP(Not substandard | Flagged) = 30/280 = 3/28\n\nEV(A) = (25/28) × 300 = 7500/28 ≈ €267.86\n\nEV(B) = (3/28) × 2500 = 7500/28 ≈ €267.86\n\n**The expected values are exactly equal** (both equal 7500/28). This makes sense: since the payout ratio (2500/300 ≈ 8.33) is close to the inverse odds ratio (25/3 ≈ 8.33), the bets are nearly balanced.\n\nGiven the exact tie in expected value, I'll choose based on lower variance/risk. Option A has a much higher probability of winning (89.3% vs 10.7%), making it the safer, more reliable choice despite identical expected value.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 66, "outcome": "valid", "prompt_sha256": "c1e82eab26d5df7a9852f1e92f6e1ca68bff0a934ea516423d32faf16988f5bc", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(fraudulent | not flagged).\n\n**Setting up the numbers** (per 10,000 payments):\n- Fraudulent: 2% = 200 payments — all flagged (since detector never misses)\n- Not fraudulent: 9,800 payments — 5% flagged = 490 flagged, 9,310 not flagged\n\n**Not flagged total:** 0 (fraudulent) + 9,310 (not fraudulent) = 9,310\n\nSo P(fraudulent | not flagged) = 0 / 9,310 = 0\n\nSince the detector **never misses** any fraudulent payment, if a payment is not flagged, it is **certainly not fraudulent** (probability of fraud = 0%).\n\n**Expected values:**\n- Option A: 0% × €490 = €0\n- Option B: 100% × €200 = €200\n\n**DECISION: B**", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 67, "outcome": "valid", "prompt_sha256": "cc0e9f178186b844e218cc8e0a627d15c3c1934daeddf90f476d1db13a84bd6d", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(infected | flagged) using Bayes' theorem.\n\n**Setup:**\n- P(infected) = 0.01, P(not infected) = 0.99\n- P(flagged | infected) = 1.00 (perfect sensitivity)\n- P(flagged | not infected) = 0.10 (false positive rate)\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged|infected)×P(infected) + P(flagged|not infected)×P(not infected)\n= 1.00 × 0.01 + 0.10 × 0.99\n= 0.01 + 0.099\n= 0.109\n\n**Calculate posterior probabilities:**\nP(infected | flagged) = 0.01 / 0.109 ≈ 0.0917 (about 9.2%)\nP(not infected | flagged) = 0.099 / 0.109 ≈ 0.9083 (about 90.8%)\n\n**Expected value of each bet:**\n\nOption A (bet infected): \nEV = 0.0917 × €990 + 0.9083 × €0 ≈ €90.80\n\nOption B (bet not infected):\nEV = 0.9083 × €100 + 0.0917 × €0 ≈ €90.83\n\n**Comparison:**\nThe expected values are extremely close (€90.80 vs €90.83), with Option B very slightly higher. This makes sense: the payouts (€990 vs €100) are roughly proportional to the inverse odds (0.092 vs 0.908), so the bet is nearly fair either way — but B has a tiny edge.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 68, "outcome": "valid", "prompt_sha256": "f534643ecf2e0dcc48baa38f492838fe83cd09ed3ec5199d98f9bcccda06b61a", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(faulty | flagged) using Bayes' theorem.\n\n**Setting up the base rates:**\n- P(faulty) = 0.03\n- P(not faulty) = 0.97\n- P(flagged | faulty) = 1.0 (never misses)\n- P(flagged | not faulty) = 0.20 (false positive rate)\n\n**Calculating P(flagged):**\nP(flagged) = P(flagged | faulty) × P(faulty) + P(flagged | not faulty) × P(not faulty)\nP(flagged) = (1.0 × 0.03) + (0.20 × 0.97)\nP(flagged) = 0.03 + 0.194\nP(flagged) = 0.224\n\n**Calculating posterior probabilities:**\nP(faulty | flagged) = 0.03 / 0.224 ≈ 0.1339 (about 13.4%)\nP(not faulty | flagged) = 0.194 / 0.224 ≈ 0.8661 (about 86.6%)\n\n**Calculating expected values:**\n\nOption A (bet faulty):\nEV(A) = 0.1339 × €1,940 + 0.8661 × €0\nEV(A) ≈ €259.8\n\nOption B (bet not faulty):\nEV(B) = 0.8661 × €300 + 0.1339 × €0\nEV(B) ≈ €259.8\n\nThese are remarkably close. Let me compute more precisely using fractions:\n- P(faulty | flagged) = 3/224\n- P(not faulty | flagged) = 194/224\n\nEV(A) = (3/224) × 1940 = 5820/224 ≈ €25.98... \n\nWait, let me redo this — 0.03 × 1940 = 58.2, not the fraction directly. Recalculating:\n\nEV(A) = (0.03/0.224) × 1940 = (0.03 × 1940)/0.224 = 58.2/0.224 ≈ €259.82\n\nEV(B) = (0.194/0.224) × 300 = 58.2/0.224 ≈ €259.82\n\n**They are exactly equal!** (Since 0.03 × 1940 = 58.2 and 0.194 × 300 = 58.2)\n\nSince the expected values are identical, I'll choose based on lower variance/higher probability of winning — Option B wins about 86.6% of the time versus Option A's 13.4%, making it the safer, more reliable choice with the same expected return.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 69, "outcome": "valid", "prompt_sha256": "5bb1a338b86f7b77fc5603fab3e37b348ea6718f2641514b717e487233fc2669", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(fake | flagged) using Bayes' theorem.\n\n**Setting up the numbers** (using 1,000 reviews as base):\n- Fake reviews: 10% = 100 reviews → all flagged (100 flagged)\n- Not fake reviews: 90% = 900 reviews → 20% wrongly flagged = 180 flagged\n\n**Total flagged:** 100 + 180 = 280\n\n**P(fake | flagged)** = 100/280 = 5/14 ≈ 35.7%\n\n**P(not fake | flagged)** = 180/280 = 9/14 ≈ 64.3%\n\n**Calculating expected values:**\n\nOption A (bet fake): \nEV = (5/14) × €1,800 = €642.86\n\nOption B (bet not fake):\nEV = (9/14) × €1,000 = €642.86\n\nThese are remarkably close — let me compute precisely:\n\n- EV(A) = 1800 × 100/280 = 180,000/280 = €642.857...\n- EV(B) = 1000 × 180/280 = 180,000/280 = €642.857...\n\n**The expected values are exactly equal!**\n\nSince both options yield identical expected value (€642.86), there's no strictly better choice on EV grounds alone. However, Option A requires the less probable event (35.7%) to pay off, while Option B relies on the more probable event (64.3%) — meaning Option B wins more often, even though the amounts balance out. Given that many decision-makers prefer higher probability of winning when expected values tie, and Option B offers a \"safer\" bet (more likely to win, just for less money), I'll choose based on the win rate.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 70, "outcome": "valid", "prompt_sha256": "243f9e0008d0a943dc217f4f29e2cbc7e1271d2f968b21fa67d80cd935a312ec", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(fraudulent | flagged) using Bayes' theorem.\n\n**Setup:**\n- P(Fraud) = 0.02, P(Not Fraud) = 0.98\n- P(Flag | Fraud) = 1.00\n- P(Flag | Not Fraud) = 0.05\n\n**Calculate P(Flag):**\nP(Flag) = P(Flag|Fraud)×P(Fraud) + P(Flag|Not Fraud)×P(Not Fraud)\nP(Flag) = (1.00 × 0.02) + (0.05 × 0.98)\nP(Flag) = 0.02 + 0.049 = 0.069\n\n**Calculate posterior probabilities:**\nP(Fraud | Flag) = 0.02 / 0.069 ≈ 0.2899 (about 29%)\nP(Not Fraud | Flag) = 0.049 / 0.069 ≈ 0.7101 (about 71%)\n\n**Calculate expected values:**\n\nOption A (bet fraudulent):\nEV(A) = 0.2899 × €490 + 0.7101 × €0 ≈ €142.03\n\nOption B (bet not fraudulent):\nEV(B) = 0.7101 × €200 + 0.2899 × €0 ≈ €142.03\n\nThese are remarkably close. Let me compute more precisely using fractions:\n\nP(Fraud|Flag) = 0.02/0.069 = 20/69\nP(Not Fraud|Flag) = 49/69\n\nEV(A) = (20/69) × 490 = 9800/69 ≈ €142.03\n\nEV(B) = (49/69) × 200 = 9800/69 ≈ €142.03\n\n**The expected values are exactly equal** (9800/69 in both cases)! This is essentially a tie, so either choice is mathematically equivalent. Given the exact tie, I'll go with the option that has the higher probability of winning (lower variance), which is Option B (71% chance of winning vs 29%).\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 71, "outcome": "valid", "prompt_sha256": "3d113eb09425e433c7871a2659023ec3a3b3cf215fe05eccd712bde9688d3bbf", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(spam | flagged) using Bayes' theorem.\n\n**Given information:**\n- P(spam) = 0.20, P(not spam) = 0.80\n- P(flagged | spam) = 1.00 (never misses)\n- P(flagged | not spam) = 0.10 (false positive rate)\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged|spam)×P(spam) + P(flagged|not spam)×P(not spam)\nP(flagged) = 1.00×0.20 + 0.10×0.80\nP(flagged) = 0.20 + 0.08 = 0.28\n\n**Calculate posterior probabilities:**\n\nP(spam | flagged) = (1.00×0.20)/0.28 = 0.20/0.28 ≈ 0.714\n\nP(not spam | flagged) = (0.10×0.80)/0.28 = 0.08/0.28 ≈ 0.286\n\n**Calculate expected values:**\n\nOption A (bet spam): \nEV = 0.714 × €800 + 0.286 × €0 = €571.4\n\nOption B (bet not spam):\nEV = 0.286 × €2,000 + 0.714 × €0 = €572.0\n\n**Comparison:**\nThe expected values are very close (€571.4 vs €572.0), with Option B slightly higher.\n\nLet me verify with exact fractions: P(spam|flagged) = 5/7, P(not spam|flagged) = 2/7\n\n- Option A: (5/7)×800 = 4000/7 ≈ €571.43\n- Option B: (2/7)×2000 = 4000/7 ≈ €571.43\n\nThey are **exactly equal** — both give expected value of €4000/7 ≈ €571.43.\n\nSince the expected values are identical, I'll choose based on lower risk/variance. Option A wins more often (71.4% chance of winning €800) versus Option B (only 28.6% chance of winning €2000, but a bigger prize). Given equal expected value, Option A offers a higher probability of winning something, which is generally preferable for reducing variance.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 72, "outcome": "valid", "prompt_sha256": "b5ba0c9f97b791e8485189ef1a90328ccd67859b00ac52d441c487f1c502bdf1", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to calculate the probability that the shipment is mislabeled given that it was flagged, then compare expected values.\n\n**Setting up the base rates:**\n- P(mislabeled) = 0.08\n- P(not mislabeled) = 0.92\n- P(flagged | mislabeled) = 1.00\n- P(flagged | not mislabeled) = 0.25\n\n**Applying Bayes' theorem:**\n\nP(flagged) = P(flagged | mislabeled) × P(mislabeled) + P(flagged | not mislabeled) × P(not mislabeled)\n\nP(flagged) = 1.00 × 0.08 + 0.25 × 0.92 = 0.08 + 0.23 = 0.31\n\nP(mislabeled | flagged) = 0.08 / 0.31 ≈ 0.2581 (about 25.8%)\n\nP(not mislabeled | flagged) = 0.23 / 0.31 ≈ 0.7419 (about 74.2%)\n\n**Calculating expected values:**\n\nOption A (bet mislabeled):\nEV = 0.2581 × €2,300 + 0.7419 × €0 ≈ €593.5\n\nOption B (bet not mislabeled):\nEV = 0.7419 × €800 + 0.2581 × €0 ≈ €593.5\n\n**Comparing precisely:**\n\nEV(A) = (8/31) × 2300 = 18400/31 ≈ €593.55\n\nEV(B) = (23/31) × 800 = 18400/31 ≈ €593.55\n\nThese are exactly equal! Both options have identical expected value.\n\nSince the expected values are the same, I'll choose based on lower variance/risk — Option B has a much higher probability of winning (74.2% vs 25.8%), making it the safer, more consistent choice, even though the payouts even out.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 73, "outcome": "valid", "prompt_sha256": "d09ca602797ec63dce6328f6b2ce2a34bdc735aa38fc1d127478321d9f52b437", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(failing | flagged) using Bayes' theorem.\n\n**Given information:**\n- P(failing) = 0.20, P(not failing) = 0.80\n- P(flagged | failing) = 1.00 (never misses)\n- P(flagged | not failing) = 0.25 (false positive rate)\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged | failing) × P(failing) + P(flagged | not failing) × P(not failing)\nP(flagged) = (1.00 × 0.20) + (0.25 × 0.80)\nP(flagged) = 0.20 + 0.20 = 0.40\n\n**Calculate P(failing | flagged):**\nP(failing | flagged) = P(flagged | failing) × P(failing) / P(flagged)\nP(failing | flagged) = (1.00 × 0.20) / 0.40\nP(failing | flagged) = 0.20 / 0.40 = 0.50\n\n**Calculate P(not failing | flagged):**\nP(not failing | flagged) = 1 − 0.50 = 0.50\n\n**Expected values:**\n- Option A (bet failing): 0.50 × €2,000 = €1,000\n- Option B (bet not failing): 0.50 × €2,000 = €1,000\n\nThe probabilities are exactly equal (50/50), so both options have identical expected value. Since a decision is required, I'll note that despite the high false-negative-free detection, the high false-positive rate combined with the low base rate makes the flagged drive equally likely to be healthy or failing. With a genuine tie, I'll choose Option A, as betting on the condition the test was designed to detect is a reasonable tiebreaker.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 74, "outcome": "valid", "prompt_sha256": "459b9c2c8b450defae7621aa306b33f031f2faab5f4153619573c00593674a20", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to calculate the posterior probability that a flagged banknote is counterfeit.\n\n**Setting up the numbers:**\n- Out of 1,000 banknotes: 50 counterfeit, 950 genuine\n- All 50 counterfeit ones are flagged (true positives)\n- Of the 950 genuine ones, 19 are wrongly flagged (false positives)\n\n**Total flagged banknotes:** 50 + 19 = 69\n\n**Probability counterfeit given flagged:** 50/69 ≈ 0.7246\n\n**Probability not counterfeit given flagged:** 19/69 ≈ 0.2754\n\n**Calculating expected values:**\n\nOption A (bet counterfeit): \n- EV = (50/69) × €190 + (19/69) × €0\n- EV = 0.7246 × 190 ≈ €137.68\n\nOption B (bet not counterfeit):\n- EV = (19/69) × €500 + (50/69) × €0\n- EV = 0.2754 × 500 ≈ €137.68\n\n**Interesting — let's check exact fractions:**\n\nOption A: (50 × 190)/69 = 9500/69 ≈ €137.68\n\nOption B: (19 × 500)/69 = 9500/69 ≈ €137.68\n\nThe expected values are **exactly equal** (9500/69 in both cases)! This makes sense because the payouts (190 and 500) were likely chosen to offset the odds (50:19) exactly, since 50×190 = 19×500 = 9500.\n\nSince both options have identical expected value, the choice is technically indifferent on EV grounds. However, if I must pick one, Option B offers the same expected return with a higher probability of winning (27.5% chance of winning €500) versus Option A (72.5% chance of winning €190) — meaning Option A has lower variance and a higher chance of some payout, which may be preferable for consistent outcomes.\n\nGiven the tie in EV, I'll choose based on the higher probability of collecting a reward at all, favoring the safer, more frequent payout.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 75, "outcome": "valid", "prompt_sha256": "dd76fc6660d9652e11312ca89f6980500e8491beb4a07e80fa9c95861b076ce5", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(diseased | flagged) using Bayes' theorem.\n\n**Given information:**\n- P(diseased) = 0.05\n- P(not diseased) = 0.95\n- P(flagged | diseased) = 1.0 (never misses)\n- P(flagged | not diseased) = 0.20 (false positive rate)\n\n**Calculate P(flagged):**\n\nP(flagged) = P(flagged|diseased)×P(diseased) + P(flagged|not diseased)×P(not diseased)\n\nP(flagged) = (1.0 × 0.05) + (0.20 × 0.95)\n\nP(flagged) = 0.05 + 0.19 = 0.24\n\n**Calculate posterior probabilities:**\n\nP(diseased | flagged) = 0.05 / 0.24 ≈ 0.2083 (about 20.8%)\n\nP(not diseased | flagged) = 0.19 / 0.24 ≈ 0.7917 (about 79.2%)\n\n**Calculate expected values:**\n\n**Option A** (bet diseased): \nEV = 0.2083 × €1,900 + 0.7917 × €0 ≈ **€395.8**\n\n**Option B** (bet not diseased):\nEV = 0.7917 × €500 + 0.2083 × €0 ≈ **€395.8**\n\n**Comparison:**\n\nBoth expected values are essentially identical (€395.83 vs €395.83) — this is remarkably close. Let me verify precisely:\n\n- EV(A) = (0.05/0.24) × 1900 = 95/0.24 = 395.83...\n- EV(B) = (0.19/0.24) × 500 = 95/0.24 = 395.83...\n\nThey are **exactly equal** — both equal 95/0.24 = €395.83.\n\nSince the expected values are exactly tied, either option is mathematically equivalent. However, Option B carries less risk (79% chance of winning something vs. 21% chance), making it preferable for a risk-averse decision-maker, and it also has a higher probability of actually winning the bet.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 76, "outcome": "valid", "prompt_sha256": "d6839c1e3950159487c00460604180a0230a29675696aa26e641caf9b6d36389", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(counterfeit | not flagged) and P(not counterfeit | not flagged).\n\n**Setting up the numbers (per 10,000 banknotes):**\n- Counterfeit: 5% = 500 banknotes\n- Genuine: 95% = 9,500 banknotes\n\n**Flagging behavior:**\n- Counterfeit banknotes: 100% flagged → 500 flagged, 0 not flagged\n- Genuine banknotes: 2% falsely flagged → 190 flagged, 9,310 not flagged\n\n**Given \"not flagged\":**\n- Not flagged total = 0 + 9,310 = 9,310\n- Of these, counterfeit = 0\n- Of these, genuine = 9,310\n\nSince the detector never misses a counterfeit (100% sensitivity), a \"not flagged\" result means the banknote is **certainly genuine** (probability of counterfeit = 0).\n\n**Expected values:**\n- Option A (bet counterfeit): P(counterfeit) × €190 = 0 × €190 = €0\n- Option B (bet not counterfeit): P(not counterfeit) × €500 = 1 × €500 = €500\n\nOption B is clearly superior — in fact, it's a certain win given the detector's perfect sensitivity for counterfeits.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 77, "outcome": "valid", "prompt_sha256": "48f611d8dc1dc690e396d0cbc764257f3fdb78ec9dae03ff4f3f9c773595b373", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(counterfeit | flagged) using Bayes' theorem.\n\n**Given:**\n- P(counterfeit) = 0.05, P(not counterfeit) = 0.95\n- P(flagged | counterfeit) = 1.00\n- P(flagged | not counterfeit) = 0.02\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged|counterfeit)·P(counterfeit) + P(flagged|not counterfeit)·P(not counterfeit)\nP(flagged) = (1.00)(0.05) + (0.02)(0.95)\nP(flagged) = 0.05 + 0.019 = 0.069\n\n**Calculate posterior probabilities:**\n\nP(counterfeit | flagged) = (1.00 × 0.05) / 0.069 = 0.05 / 0.069 ≈ 0.7246\n\nP(not counterfeit | flagged) = 0.019 / 0.069 ≈ 0.2754\n\n**Expected values:**\n\nOption A: EV = 0.7246 × €190 + 0.2754 × €0 ≈ €137.68\n\nOption B: EV = 0.2754 × €500 + 0.7246 × €0 ≈ €137.68\n\nThese are remarkably close. Let me compute more precisely using fractions.\n\nP(counterfeit|flagged) = 50/69, P(not counterfeit|flagged) = 19/69\n\nOption A: EV = (50/69) × 190 = 9500/69 ≈ €137.68\n\nOption B: EV = (19/69) × 500 = 9500/69 ≈ €137.68\n\n**The two options have exactly equal expected value** (both equal 9500/69 ≈ €137.68).\n\nSince they're tied, I'll break the tie by considering risk: Option A has a higher probability of winning (72.5% vs 27.5%), which might be preferable for a risk-averse or practical decision-maker, though the payout is smaller. Given the exact equality in EV, either choice is technically justified, but I'll select the option with the higher win probability for practical decision-making.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 78, "outcome": "valid", "prompt_sha256": "be615b440c89c13fab9c8b5de2c3e279d2cbcce44535e78b3452e75685dd6d88", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(malicious | not flagged) and P(not malicious | not flagged).\n\n**Setting up the base rates:**\n- P(malicious) = 0.02, P(not malicious) = 0.98\n- P(flagged | malicious) = 1.0, so P(not flagged | malicious) = 0\n- P(flagged | not malicious) = 0.10, so P(not flagged | not malicious) = 0.90\n\n**Key insight:** Since the scanner never misses a malicious file (100% detection rate), if a file is malicious, it is *always* flagged. This means P(not flagged | malicious) = 0.\n\n**Calculating P(malicious | not flagged):**\n\nUsing Bayes' theorem:\n- P(not flagged) = P(not flagged | malicious)×P(malicious) + P(not flagged | not malicious)×P(not malicious)\n- P(not flagged) = 0 × 0.02 + 0.90 × 0.98 = 0.882\n\nP(malicious | not flagged) = [P(not flagged | malicious) × P(malicious)] / P(not flagged) = 0 / 0.882 = **0**\n\nP(not malicious | not flagged) = 1 - 0 = **1**\n\n**Conclusion:** If a file is not flagged, it is *certainly* not malicious (given the scanner has zero false negatives). \n\n**Expected values:**\n- Option A: 0 × €980 = €0\n- Option B: 1 × €200 = €200\n\nOption B is clearly better — in fact, betting on \"not malicious\" is a certain win given the scanner's perfect sensitivity.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 79, "outcome": "valid", "prompt_sha256": "b5ba0c9f97b791e8485189ef1a90328ccd67859b00ac52d441c487f1c502bdf1", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(mislabeled | flagged) using Bayes' theorem.\n\n**Given information:**\n- P(mislabeled) = 0.08\n- P(not mislabeled) = 0.92\n- P(flagged | mislabeled) = 1.00 (never misses)\n- P(flagged | not mislabeled) = 0.25 (false positive rate)\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged | mislabeled) × P(mislabeled) + P(flagged | not mislabeled) × P(not mislabeled)\nP(flagged) = 1.00 × 0.08 + 0.25 × 0.92\nP(flagged) = 0.08 + 0.23\nP(flagged) = 0.31\n\n**Calculate P(mislabeled | flagged):**\nP(mislabeled | flagged) = P(flagged | mislabeled) × P(mislabeled) / P(flagged)\nP(mislabeled | flagged) = (1.00 × 0.08) / 0.31\nP(mislabeled | flagged) = 0.08 / 0.31\nP(mislabeled | flagged) ≈ 0.258\n\n**Calculate P(not mislabeled | flagged):**\nP(not mislabeled | flagged) = 1 - 0.258 = 0.742\n\n**Expected value calculations:**\n\nOption A (bet mislabeled):\nEV(A) = 0.258 × €2,300 + 0.742 × €0\nEV(A) ≈ €593.5\n\nOption B (bet not mislabeled):\nEV(B) = 0.742 × €800 + 0.258 × €0\nEV(B) ≈ €593.5\n\n**Comparing:** These are extremely close. Let me be more precise:\n\n- P(mislabeled | flagged) = 8/31\n- P(not mislabeled | flagged) = 23/31\n\nEV(A) = (8/31) × 2300 = 18,400/31 ≈ €593.55\nEV(B) = (23/31) × 800 = 18,400/31 ≈ €593.55\n\n**The expected values are exactly equal** (both equal 18,400/31 ≈ €593.55). This is a remarkable tie — the payouts were calibrated precisely against the odds.\n\nSince both options yield identical expected value, I'll choose based on lower variance/risk. Option B has a higher probability of winning (74.2% vs 25.8%), making it the safer bet with the same expected payout.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 80, "outcome": "valid", "prompt_sha256": "c4527de0931de69c4726f83d938c762a78ef87cb3357da30c05cef88a9856bbc", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Out of 1,000 payments: 20 fraudulent (all flagged), 980 non-fraudulent (49 wrongly flagged).\n\nTotal flagged = 20 + 49 = 69\n\nP(fraudulent | flagged) = 20/69 ≈ 0.2899\nP(not fraudulent | flagged) = 49/69 ≈ 0.7101\n\nExpected value calculations:\n\n**Option A:** (20/69) × €490 ≈ €142.03\n\n**Option B:** (49/69) × €200 ≈ €142.03\n\nThese are extremely close, so let me compute more precisely:\n\n- Option A: 20 × 490 / 69 = 9800/69 = €142.029\n- Option B: 49 × 200 / 69 = 9800/69 = €142.029\n\nThe expected values are **exactly equal** (both equal 9800/69). This isn't a coincidence — it reflects that the payouts were essentially calibrated to the odds (490 ≈ 1/(20/69) scaled, and 200 ≈ 1/(49/69) scaled similarly), making this a fair bet either way.\n\nSince both options have identical expected value, the choice between them is arbitrary from a pure EV standpoint. I'll go with Option A, as it involves catching fraud, which may carry additional non-monetary value (e.g., stopping harm), and the EV is tied anyway.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 81, "outcome": "valid", "prompt_sha256": "5d840c865ea2ed610e10403c7c4ded1fe927bca2ea8e062803002f25b7dc5bca", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(forged | flagged) using Bayes' theorem.\n\n**Given information:**\n- P(forged) = 0.05, P(not forged) = 0.95\n- P(flagged | forged) = 1.0 (never misses)\n- P(flagged | not forged) = 0.10 (false positive rate)\n\n**Calculate P(flagged):**\n\nP(flagged) = P(flagged | forged) × P(forged) + P(flagged | not forged) × P(not forged)\n\nP(flagged) = 1.0 × 0.05 + 0.10 × 0.95 = 0.05 + 0.095 = 0.145\n\n**Calculate posterior probabilities:**\n\nP(forged | flagged) = (1.0 × 0.05) / 0.145 = 0.05 / 0.145 ≈ 0.3448\n\nP(not forged | flagged) = 0.095 / 0.145 ≈ 0.6552\n\n**Calculate expected values:**\n\nOption A (bet forged): EV = 0.3448 × €950 + 0.6552 × €0 ≈ €327.60\n\nOption B (bet not forged): EV = 0.6552 × €500 + 0.3448 × €0 ≈ €327.60\n\nThese are remarkably close. Let me compute more precisely using fractions.\n\nP(forged | flagged) = 0.05/0.145 = 50/145 = 10/29\nP(not forged | flagged) = 95/145 = 19/29\n\nEV(A) = (10/29) × 950 = 9500/29 ≈ €327.59\n\nEV(B) = (19/29) × 500 = 9500/29 ≈ €327.59\n\n**The expected values are exactly equal** (both equal 9500/29 ≈ €327.59). This is a tie in expected value terms.\n\nGiven the exact tie, I'll consider that Option B carries lower risk (higher probability of winning, ~65.5% vs ~34.5%), which may be preferable for risk-averse decision-making, but since the question asks purely about betting on \"the truth\" with equal expected value, either choice is mathematically justified. I'll select the option aligned with the higher-probability outcome.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 82, "outcome": "valid", "prompt_sha256": "b4e63fb16053da850e3c398d76303afda602c6a7f9f142539074f8cd4a6b391a", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(failing | flagged) using Bayes' theorem.\n\n**Given information:**\n- P(failing) = 0.20, so P(not failing) = 0.80\n- P(flagged | failing) = 1.00 (never misses)\n- P(flagged | not failing) = 0.25 (false positive rate)\n\n**Calculate P(flagged):**\n\nP(flagged) = P(flagged | failing) × P(failing) + P(flagged | not failing) × P(not failing)\n\nP(flagged) = (1.00 × 0.20) + (0.25 × 0.80)\n\nP(flagged) = 0.20 + 0.20 = 0.40\n\n**Calculate P(failing | flagged):**\n\nP(failing | flagged) = [P(flagged | failing) × P(failing)] / P(flagged)\n\nP(failing | flagged) = (1.00 × 0.20) / 0.40 = 0.50\n\n**Calculate P(not failing | flagged):**\n\nP(not failing | flagged) = 1 - 0.50 = 0.50\n\n**Expected values:**\n\n- Option A (bet failing): 0.50 × €2,000 = €1,000\n- Option B (bet not failing): 0.50 × €2,000 = €1,000\n\nBoth options have identical expected value (€1,000), since the probability is exactly 50/50. There's no strict preference based on expected value alone — I'll choose Option A as a tie-breaker since it aligns with the flagged status of the drive.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 83, "outcome": "valid", "prompt_sha256": "54a1949e0160a75b7078c3f72f519c98c89804dc02861b67a76d86abe7076eb5", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(contaminated | flagged) using Bayes' theorem.\n\n**Setup:**\n- P(contaminated) = 0.10, P(not contaminated) = 0.90\n- P(flagged | contaminated) = 1.0 (never misses)\n- P(flagged | not contaminated) = 0.10 (false positive rate)\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged | contaminated) × P(contaminated) + P(flagged | not contaminated) × P(not contaminated)\nP(flagged) = (1.0 × 0.10) + (0.10 × 0.90)\nP(flagged) = 0.10 + 0.09 = 0.19\n\n**Calculate posterior probabilities:**\n\nP(contaminated | flagged) = (1.0 × 0.10) / 0.19 = 0.10 / 0.19 ≈ 0.5263\n\nP(not contaminated | flagged) = (0.10 × 0.90) / 0.19 = 0.09 / 0.19 ≈ 0.4737\n\n**Calculate expected values:**\n\nOption A (bet contaminated): 0.5263 × €900 ≈ €473.68\n\nOption B (bet not contaminated): 0.4737 × €1,000 ≈ €473.68\n\nThese are essentially equal (the small rounding difference is just from decimal truncation). Let me verify with exact fractions:\n\n- Option A: (10/19) × 900 = 9000/19 ≈ €473.68\n- Option B: (9/19) × 1000 = 9000/19 ≈ €473.68\n\n**They are exactly equal!** Both options have identical expected value of €9000/19 ≈ €473.68.\n\nSince the expected values are precisely tied, either choice is mathematically equivalent. I'll choose Option A, since it directly bets on the more probable outcome (contaminated has a slightly higher posterior probability, 52.6% vs 47.4%), even though the payout structure equalizes the expected value.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 84, "outcome": "valid", "prompt_sha256": "c1e82eab26d5df7a9852f1e92f6e1ca68bff0a934ea516423d32faf16988f5bc", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(fraudulent | not flagged) and P(not fraudulent | not flagged).\n\n**Setting up the base rates:**\n- P(fraud) = 0.02, P(not fraud) = 0.98\n- P(flagged | fraud) = 1.00, so P(not flagged | fraud) = 0\n- P(flagged | not fraud) = 0.05, so P(not flagged | not fraud) = 0.95\n\n**Key insight:** Since the system never misses fraud (100% detection rate), if a payment is fraudulent, it is *always* flagged. Therefore, if a payment is **not flagged**, it **cannot** be fraudulent.\n\nP(not flagged | fraud) = 0, so:\n\nP(fraud | not flagged) = 0\n\nP(not fraud | not flagged) = 1\n\n**Evaluating the bets:**\n\n- Option A: Bet fraudulent. Probability of winning = 0. Expected value = €0.\n- Option B: Bet not fraudulent. Probability of winning = 1. Expected value = €200.\n\nOption B is certain to win, since a non-flagged payment is guaranteed (by the system's perfect fraud-detection) to be non-fraudulent.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 85, "outcome": "valid", "prompt_sha256": "8d2419773dc4d9eee674234ed73a5509e5d6904405c22e80350a615d1c55b1c7", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to calculate the probability that the painting is forged given that it was flagged, then compare expected values.\n\n**Setting up the probabilities (Bayes' theorem):**\n\n- P(Forged) = 0.05, P(Not Forged) = 0.95\n- P(Flagged | Forged) = 1.0 (never misses)\n- P(Flagged | Not Forged) = 0.10 (false positive rate)\n\n**Calculating P(Flagged):**\n\nP(Flagged) = P(Flagged|Forged)×P(Forged) + P(Flagged|Not Forged)×P(Not Forged)\nP(Flagged) = (1.0 × 0.05) + (0.10 × 0.95)\nP(Flagged) = 0.05 + 0.095 = 0.145\n\n**Calculating P(Forged | Flagged):**\n\nP(Forged | Flagged) = (1.0 × 0.05) / 0.145 = 0.05 / 0.145 ≈ 0.3448\n\nP(Not Forged | Flagged) = 1 - 0.3448 ≈ 0.6552\n\n**Expected values for each option:**\n\nOption A (bet forged):\nEV(A) = 0.3448 × €950 = €327.59\n\nOption B (bet not forged):\nEV(B) = 0.6552 × €500 = €327.59\n\n**Comparison:**\n\nThese are remarkably close — let me compute precisely with fractions.\n\nP(Forged|Flagged) = 0.05/0.145 = 10/29\nP(Not Forged|Flagged) = 19/29\n\nEV(A) = (10/29) × 950 = 9500/29 ≈ €327.59\nEV(B) = (19/29) × 500 = 9500/29 ≈ €327.59\n\n**They are exactly equal!** Both options yield the same expected value of €9500/29 ≈ €327.59.\n\nSince the expected values are identical, I'll choose based on risk: Option A has a lower probability of winning (34.5%) but a higher payout, while Option B has a higher probability of winning (65.5%) with a lower payout. Given the mathematical tie, I'll opt for the choice with the higher probability of success, favoring the more likely outcome.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 86, "outcome": "valid", "prompt_sha256": "c27858d32012507c928b2802cb018e26ad6ce103a769b742342bab807f0f0f5d", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(diseased | flagged) using Bayes' theorem.\n\n**Setup:**\n- P(diseased) = 0.05, P(not diseased) = 0.95\n- P(flagged | diseased) = 1.0\n- P(flagged | not diseased) = 0.20\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged|diseased)×P(diseased) + P(flagged|not diseased)×P(not diseased)\n= 1.0×0.05 + 0.20×0.95\n= 0.05 + 0.19\n= 0.24\n\n**Calculate posterior probabilities:**\n\nP(diseased | flagged) = (1.0 × 0.05) / 0.24 = 0.05/0.24 ≈ 0.2083\n\nP(not diseased | flagged) = (0.20 × 0.95) / 0.24 = 0.19/0.24 ≈ 0.7917\n\n**Calculate expected values:**\n\nOption A (bet diseased): EV = 0.2083 × €1,900 ≈ €395.8\n\nOption B (bet not diseased): EV = 0.7917 × €500 ≈ €395.8\n\nThese are remarkably close — let me compute exactly:\n\n- EV(A) = (0.05/0.24) × 1900 = 95/0.24... let's use fractions: (5/120)×1900 = 9500/120 ≈ €79.17... \n\nLet me redo this carefully with fractions (using 0.05 = 1/20, 0.95=19/20):\n\nP(flagged) = 1×(1/20) + 0.2×(19/20) = 1/20 + 19/100 = 5/100+19/100 = 24/100\n\nP(diseased|flagged) = (1/20)/(24/100) = (5/100)/(24/100) = 5/24\nP(not diseased|flagged) = 19/24\n\nEV(A) = (5/24)×1900 = 9500/24 ≈ €395.83\nEV(B) = (19/24)×500 = 9500/24 ≈ €395.83\n\n**They are exactly equal!** Both options have identical expected value of €9500/24 ≈ €395.83.\n\nSince the expected values are tied, I'll choose based on lower variance/risk. Option B wins with probability 19/24 (~79%), a much higher chance of winning something, compared to Option A's 5/24 (~21%) chance. Given equal expected value, the safer bet with higher win probability is preferable.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 87, "outcome": "valid", "prompt_sha256": "3703f20b1c21be89e3020cf0198bd83ed804f51e9eb9aa0a4951c0f08f876156", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(contaminated | not flagged).\n\n**Setting up the numbers** (using 1000 samples):\n- Contaminated: 100, Not contaminated: 900\n- Test never misses contamination → all 100 contaminated samples are flagged\n- Test flags 10% of non-contaminated: 90 flagged, 810 not flagged\n\n**Not flagged samples:** 0 contaminated + 810 not contaminated = 810 total\n\nSince the test never misses a contaminated sample, a \"not flagged\" result means the sample is **certainly not contaminated** (P = 0).\n\n**Expected values:**\n- Option A: 0 × €900 = €0\n- Option B: 1 × €1,000 = €1,000\n\nOption B is clearly better — in fact, given a negative result, contamination is impossible under this model.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 88, "outcome": "valid", "prompt_sha256": "be615b440c89c13fab9c8b5de2c3e279d2cbcce44535e78b3452e75685dd6d88", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find the probability the file is malicious, given that it was NOT flagged.\n\n**Setting up the numbers (per 1000 files):**\n- Malicious: 2% = 20 files\n- Not malicious: 98% = 980 files\n\n**Applying the detection rates:**\n- Malicious files: 100% get flagged → 20 flagged, 0 not flagged\n- Not malicious files: 10% wrongly flagged → 98 flagged, 882 not flagged\n\n**Among files not flagged:**\n- Total not flagged = 0 + 882 = 882\n- Malicious among these = 0\n\nSince the scanner \"never misses\" any malicious file, **every malicious file gets flagged**. This means if a file is *not* flagged, it is **guaranteed to be not malicious** (probability of malicious = 0%).\n\n**Expected values:**\n- Option A (bet malicious): P(malicious) × €980 = 0 × €980 = €0\n- Option B (bet not malicious): P(not malicious) × €200 = 1 × €200 = €200\n\nOption B is clearly better — in fact, it's a certain win given the problem's assumptions.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 89, "outcome": "valid", "prompt_sha256": "524bbcdb9abb96cc9225de1d189398c203dc9a8beb97271fcf1b172b9618d435", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(defective | flagged) using Bayes' theorem.\n\n**Setting up the numbers:**\n- P(defective) = 0.04, P(not defective) = 0.96\n- P(flagged | defective) = 1.00 (never misses)\n- P(flagged | not defective) = 0.05 (false positive rate)\n\n**Calculating P(flagged):**\nP(flagged) = P(flagged|defective)×P(defective) + P(flagged|not defective)×P(not defective)\nP(flagged) = (1.00)(0.04) + (0.05)(0.96)\nP(flagged) = 0.04 + 0.048 = 0.088\n\n**Calculating posterior probabilities:**\n- P(defective | flagged) = 0.04 / 0.088 = 0.4545 (about 45.5%)\n- P(not defective | flagged) = 0.048 / 0.088 = 0.5455 (about 54.5%)\n\n**Calculating expected values:**\n\nOption A (bet defective): \nEV = 0.4545 × €480 + 0.5455 × €0 = €218.18\n\nOption B (bet not defective):\nEV = 0.5455 × €400 + 0.4545 × €0 = €218.18\n\nThese are remarkably close — let me compute more precisely using fractions.\n\nP(defective|flagged) = 4/8.8 = 5/11\nP(not defective|flagged) = 4.8/8.8 = 6/11\n\nEV(A) = (5/11) × 480 = 2400/11 = €218.18\nEV(B) = (6/11) × 400 = 2400/11 = €218.18\n\n**The expected values are exactly equal!** In this case, I need a tie-breaker. Since the outcomes are identical in expectation, I'll consider risk: Option B has a higher probability of winning (54.5% vs 45.5%), meaning more consistent/reliable payouts, even though the amounts are structured to equalize EV.\n\nGiven the tie in expected value, I lean toward the option with higher win probability for practical decision-making.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 90, "outcome": "valid", "prompt_sha256": "76e41241191b29141c4cb6961bf94242322f56d117ddee81c44598b653f0833f", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Out of 1,000 parts: 40 defective (all flagged) + 48 falsely flagged = 88 total flagged.\n\nP(defective | flagged) = 40/88 ≈ 0.4545\nP(not defective | flagged) = 48/88 ≈ 0.5455\n\nExpected value calculations:\n\n**Option A:** EV = 0.4545 × €480 ≈ €218.18\n\n**Option B:** EV = 0.5455 × €400 ≈ €218.18\n\nThese are remarkably close — let me verify precisely:\n- EV(A) = (40/88) × 480 = 19,200/88 = €218.18\n- EV(B) = (48/88) × 400 = 19,200/88 = €218.18\n\nThey're exactly equal! The expected values are identical, so purely on EV grounds, it's a toss-up. However, since the numbers are precisely balanced, I'll consider a tiebreaker: Option B has a higher probability of winning (54.5% vs 45.5%), which means more consistent/frequent payouts, even though the amount is smaller. For someone preferring a higher chance of winning something (and given true indifference in EV), Option B is the slightly more intuitive choice due to the higher win probability.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 91, "outcome": "valid", "prompt_sha256": "42bd12c3bf417dcb92506c2aad9e77813911a1a752763b05c07431fbebbdf598", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Let me calculate the probabilities and expected values.\n\n**Setting up the numbers:**\n- Diseased trees flagged: 50 (all diseased trees are caught)\n- Healthy trees flagged: 190 (false positives)\n- Total flagged: 240\n\n**Probability calculations given a flag:**\n- P(diseased | flagged) = 50/240 ≈ 0.2083\n- P(not diseased | flagged) = 190/240 ≈ 0.7917\n\n**Expected value of Option A (bet diseased):**\nEV(A) = (50/240) × €1,900 = €395.83\n\n**Expected value of Option B (bet not diseased):**\nEV(B) = (190/240) × €500 = €395.83\n\nInteresting — the expected values are essentially **equal** (both €395.83). The payouts were set to exactly offset the probability differences.\n\nSince Option A and Option B have the same expected value, the choice is a toss-up on pure EV grounds. However, I'll note that Option B has a much higher probability of winning (79% vs 21%), even though the payout is smaller. If we have any preference for consistency/lower variance, or if there's uncertainty in the exact numbers, Option B's higher win-rate makes it a more comfortable bet with the same expected return.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 92, "outcome": "valid", "prompt_sha256": "243f9e0008d0a943dc217f4f29e2cbc7e1271d2f968b21fa67d80cd935a312ec", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(fraudulent | flagged) using Bayes' theorem.\n\n**Setting up the numbers** (using 10,000 payments):\n- Fraudulent: 2% = 200 payments → all 200 flagged (no misses)\n- Not fraudulent: 9,800 payments → 5% wrongly flagged = 490 flagged\n\n**Total flagged** = 200 + 490 = 690\n\n**Probabilities given flagged:**\n- P(fraudulent | flagged) = 200/690 ≈ 0.2899\n- P(not fraudulent | flagged) = 490/690 ≈ 0.7101\n\n**Expected values:**\n\nOption A: 0.2899 × €490 ≈ €142.03\n\nOption B: 0.7101 × €200 ≈ €142.03\n\nThese are essentially equal (the small difference is rounding). Let me compute exactly:\n\n- Option A: (200/690) × 490 = 98,000/690 ≈ €142.03\n- Option B: (490/690) × 200 = 98,000/690 ≈ €142.03\n\n**They are exactly equal!** Both options yield the same expected value of €98,000/690 ≈ €142.03.\n\nSince the expected values are identical, I'll choose based on lower variance/risk. Option B wins with probability ~71%, making it the safer bet with a higher chance of a (smaller) payout, while Option A is a lower-probability, higher-payout bet. Given equal expected value, the higher win-probability option is generally preferable for risk-aversion.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 93, "outcome": "valid", "prompt_sha256": "459b9c2c8b450defae7621aa306b33f031f2faab5f4153619573c00593674a20", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to calculate the probability of each outcome given the banknote was flagged.\n\n**Setting up the numbers:**\n- Total banknotes: 1,000\n- Counterfeit: 50, all flagged (true positives = 50)\n- Genuine: 950, of which 19 are wrongly flagged (false positives = 19)\n- Total flagged: 50 + 19 = 69\n\n**Probabilities given \"flagged\":**\n- P(counterfeit | flagged) = 50/69 ≈ 0.7246\n- P(not counterfeit | flagged) = 19/69 ≈ 0.2754\n\n**Expected value calculations:**\n\nOption A (bet counterfeit): Win €190 if counterfeit, €0 otherwise\n- EV(A) = (50/69) × €190 + (19/69) × €0\n- EV(A) = 9500/69 ≈ €137.68\n\nOption B (bet not counterfeit): Win €500 if not counterfeit, €0 otherwise\n- EV(B) = (19/69) × €500 + (50/69) × €0\n- EV(B) = 9500/69 ≈ €137.68\n\n**Comparison:**\n\nInterestingly, both expected values are exactly equal: 9500/69 ≈ €137.68\n\nSince the expected values are identical, either choice is mathematically equivalent in terms of expected return. However, Option A has a much higher probability of winning (72.5% vs 27.5%), even though the payout is smaller. For someone who prefers a higher chance of winning something (lower variance), Option A is preferable given the equal expected value.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 94, "outcome": "valid", "prompt_sha256": "8b079d6a529c6cae7a8ae22cd54aa89a2b6505b52f8f4f4bd8208f11c64a96f2", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(diseased | not flagged) and P(not diseased | not flagged).\n\n**Setting up base rates:**\n- P(diseased) = 0.05, P(not diseased) = 0.95\n- P(flagged | diseased) = 1.0 (never misses), so P(not flagged | diseased) = 0\n- P(flagged | not diseased) = 0.20, so P(not flagged | not diseased) = 0.80\n\n**Key insight:** Since the drone never misses a diseased tree, if a tree is diseased, it is *always* flagged. This means a tree that is **not flagged cannot be diseased**.\n\nP(not flagged | diseased) = 0, so P(diseased | not flagged) = 0.\n\nLet me verify with Bayes' theorem using a population of 1000 trees:\n- Diseased: 50 trees → all 50 flagged, 0 not flagged\n- Not diseased: 950 trees → 20% flagged = 190 flagged, 80% not flagged = 760 not flagged\n\nTotal not flagged = 0 + 760 = 760\nOf these, diseased = 0, not diseased = 760\n\n**P(diseased | not flagged) = 0/760 = 0**\n**P(not diseased | not flagged) = 760/760 = 1**\n\n**Expected values:**\n- Option A: 0 × €1,900 = €0\n- Option B: 1 × €500 = €500\n\nOption B is clearly better — in fact, given a \"not flagged\" result, the tree is certainly not diseased, so betting on disease is guaranteed to lose.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 95, "outcome": "valid", "prompt_sha256": "6fdace3427dfdbb2b6d189c0d14078861ed9473eebde135d193c7ce5a3bf2ed3", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find the probability of infection given a flagged result, then compare expected values.\n\n**Setting up the base rates:**\n- P(Infected) = 0.01, P(Not infected) = 0.99\n- P(Flagged | Infected) = 1.0 (never misses)\n- P(Flagged | Not infected) = 0.10 (false positive rate)\n\n**Using Bayes' theorem:**\n\nP(Flagged) = P(Flagged|Infected)×P(Infected) + P(Flagged|Not infected)×P(Not infected)\nP(Flagged) = (1.0)(0.01) + (0.10)(0.99)\nP(Flagged) = 0.01 + 0.099 = 0.109\n\nP(Infected | Flagged) = 0.01 / 0.109 ≈ 0.0917 (about 9.2%)\n\nP(Not infected | Flagged) = 0.099 / 0.109 ≈ 0.9083 (about 90.8%)\n\n**Calculating expected values:**\n\n**Option A** (bet infected, win €990 if correct):\nEV(A) = 0.0917 × €990 + 0.9083 × €0 ≈ €90.80\n\n**Option B** (bet not infected, win €100 if correct):\nEV(B) = 0.9083 × €100 + 0.0917 × €0 ≈ €90.83\n\n**Comparison:**\nThe expected values are remarkably close (≈€90.80 vs ≈€90.83), but Option B has a very slightly higher expected value, and it aligns with the much higher probability (91%) that the sample is actually not infected — the base rate effect dominates despite the \"perfect\" sensitivity of the test.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 96, "outcome": "valid", "prompt_sha256": "306a62e9908d78f7ef1a642257f34fc3cdd2c041a1ca8bc08afedf5edff82fb4", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(faulty | not flagged) using Bayes' theorem.\n\n**Setup:**\n- P(faulty) = 0.03\n- P(not faulty) = 0.97\n- P(flagged | faulty) = 1.00, so P(not flagged | faulty) = 0\n- P(flagged | not faulty) = 0.20, so P(not flagged | not faulty) = 0.80\n\n**Key insight:** Since the camera never misses a faulty panel (100% detection rate), if a panel is faulty, it is *always* flagged. This means if a panel is **not flagged**, it **cannot be faulty**.\n\nSo P(faulty | not flagged) = 0\n\n**Calculating expected values:**\n\n*Option A (bet faulty):* \nEV = 0 × €1,940 = €0\n\n*Option B (bet not faulty):*\nSince P(not faulty | not flagged) = 1\nEV = 1 × €300 = €300\n\n**Conclusion:** Option B is guaranteed to win, since a non-flagged panel is certainly not faulty (given the camera has perfect sensitivity for faulty panels).\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 97, "outcome": "valid", "prompt_sha256": "c2c1abdec7056e8345e193fa96af722f87cb59f124bf62e575c8548f22a5f358", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(counterfeit | flagged) using Bayes' theorem.\n\n**Given information:**\n- P(counterfeit) = 0.05, P(not counterfeit) = 0.95\n- P(flagged | counterfeit) = 1.00 (never misses)\n- P(flagged | not counterfeit) = 0.02 (false positive rate)\n\n**Calculate P(flagged):**\n\nP(flagged) = P(flagged|counterfeit)×P(counterfeit) + P(flagged|not counterfeit)×P(not counterfeit)\n\nP(flagged) = (1.00)(0.05) + (0.02)(0.95) = 0.05 + 0.019 = 0.069\n\n**Calculate posterior probabilities:**\n\nP(counterfeit | flagged) = 0.05 / 0.069 ≈ 0.7246\n\nP(not counterfeit | flagged) = 0.019 / 0.069 ≈ 0.2754\n\n**Calculate expected values:**\n\nOption A (bet counterfeit): EV = 0.7246 × €190 ≈ €137.68\n\nOption B (bet not counterfeit): EV = 0.2754 × €500 ≈ €137.68\n\nThese are very close — let me compute more precisely using fractions.\n\nP(counterfeit | flagged) = 0.05/0.069 = 50/69\nP(not counterfeit | flagged) = 19/69\n\nEV(A) = (50/69) × 190 = 9500/69 ≈ 137.68\n\nEV(B) = (19/69) × 500 = 9500/69 ≈ 137.68\n\n**The expected values are exactly equal!** This is a remarkable coincidence in the numbers. Since they're tied, I need a tiebreaker — with identical expected value, risk considerations matter. Option A has a much higher win probability (72%) with a smaller payout, while Option B has a lower win probability (28%) with a larger payout. Given equal EV, the option with higher probability of winning (lower variance) is generally preferable for most decision-makers.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 98, "outcome": "valid", "prompt_sha256": "41be9eb201bb8adefbf52e6c3982d9665135bd0fc77b389b321cfaeef4fa4f1d", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Let me calculate the probability that a flagged email is actually spam.\n\n**Setup:**\n- 200 spam emails, all flagged (200 true positives)\n- 800 non-spam emails, 80 flagged (80 false positives)\n- Total flagged = 200 + 80 = 280\n\n**P(spam | flagged) = 200/280 = 5/7 ≈ 71.4%**\n**P(not spam | flagged) = 80/280 = 2/7 ≈ 28.6%**\n\n**Expected value of each bet:**\n\nOption A (bet spam): \nEV = (5/7) × €800 + (2/7) × €0 = €4000/7 ≈ €571.43\n\nOption B (bet not spam):\nEV = (2/7) × €2000 + (5/7) × €0 = €4000/7 ≈ €571.43\n\n**The expected values are exactly equal (€571.43 each).**\n\nThis makes sense: the payouts (€800 and €2,000) are set almost inversely proportional to the odds (5:2), so both bets have identical expected value. Since there's no strict financial advantage, I'll choose based on the higher probability of winning — Option A wins about 71% of the time versus 29% for Option B, which feels more intuitive and less variance-prone even though the EV is tied.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 99, "outcome": "valid", "prompt_sha256": "865565c381afc2a87df0f232c42e4b0c9c8cf80f37c9bec56038c2f441a4cd3e", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(failing | not flagged) vs P(not failing | not flagged).\n\n**Setting up the numbers:**\n\nAssume 100 hard drives.\n- Failing: 20 drives, Not failing: 80 drives\n- All 20 failing drives get flagged (100% detection rate)\n- Of the 80 not failing drives, 25% get wrongly flagged = 20 drives flagged; 60 drives not flagged\n\n**Not flagged group:**\n- Failing but not flagged: 0 (since detection never misses)\n- Not failing and not flagged: 60\n\nTotal not flagged: 0 + 60 = 60\n\n**Calculating probabilities:**\n\nP(failing | not flagged) = 0/60 = 0%\nP(not failing | not flagged) = 60/60 = 100%\n\nSince the health check never misses a failing drive, a \"not flagged\" result guarantees the drive is not failing. Betting on \"not failing\" is a certain win.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 100, "outcome": "valid", "prompt_sha256": "d821b23f3efa0436a095e948567c4ac939a2cf5e3aa50a3993887bf8eb9a5dd4", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to calculate the expected value of each option based on the probability that a flagged sample is actually contaminated.\n\n## Setting Up the Numbers\n\nOut of 1,000 samples:\n- 100 are contaminated → all 100 are flagged (no false negatives)\n- 900 are not contaminated → 90 are wrongly flagged (false positives)\n\n**Total flagged samples:** 100 + 90 = 190\n\n## Calculating Probabilities\n\nGiven that a sample is flagged, the probability it's actually contaminated is:\n\nP(contaminated | flagged) = 100 / 190 ≈ 0.526 (52.6%)\n\nP(not contaminated | flagged) = 90 / 190 ≈ 0.474 (47.4%)\n\n## Calculating Expected Values\n\n**Option A** (bet contaminated, win €900 if correct):\nEV(A) = (100/190) × €900 = €473.68\n\n**Option B** (bet not contaminated, win €1,000 if correct):\nEV(B) = (90/190) × €1,000 = €473.68\n\n## Comparison\n\nInteresting — the expected values are essentially equal (both ≈ €473.68). The payouts (€900 vs €1,000) were calibrated to roughly offset the probability difference (52.6% vs 47.4%).\n\nLet me double check precisely:\n- EV(A) = 100 × 900 / 190 = 90,000/190 = 473.68...\n- EV(B) = 90 × 1000 / 190 = 90,000/190 = 473.68...\n\nThey are **exactly equal** — both equal 90,000/190 = €473.68.\n\nSince the expected values are identical, this is a toss-up on pure EV grounds. However, Option A has a *higher probability of winning* (52.6% vs 47.4%), meaning it wins more often, even though the amounts balance out. If there's any risk-aversion or preference for a higher win-rate (e.g., psychological, or reducing variance), Option A is slightly favorable since it aligns with the more likely outcome.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 101, "outcome": "valid", "prompt_sha256": "b355e2ee0cea29b13fc974724dadc3057f5827227b30577c2515eab2c8f60b0d", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(mislabeled | flagged) using the given numbers.\n\n**Setting up the numbers:**\n- Total shipments: 1,000\n- Mislabeled: 80, all flagged (true positives = 80)\n- Not mislabeled: 920, of which 230 are wrongly flagged (false positives = 230)\n- Total flagged = 80 + 230 = 310\n\n**Calculating probabilities given flagged:**\n- P(mislabeled | flagged) = 80/310 ≈ 0.2581\n- P(not mislabeled | flagged) = 230/310 ≈ 0.7419\n\n**Expected value of Option A (bet mislabeled):**\nEV(A) = (80/310) × €2,300 = 0.2581 × 2,300 ≈ €593.5\n\n**Expected value of Option B (bet not mislabeled):**\nEV(B) = (230/310) × €800 = 0.7419 × 800 ≈ €593.5\n\n**Checking exact values:**\n- EV(A) = (80 × 2300)/310 = 184,000/310 ≈ €593.55\n- EV(B) = (230 × 800)/310 = 184,000/310 ≈ €593.55\n\nThese are exactly equal! Both options have identical expected value of €184,000/310 ≈ €593.55.\n\nSince the expected values are precisely tied, there's no strictly better financial choice — either option is equally good in expectation. I'll choose Option A, as betting on the rarer but correctly-identified condition (mislabeling) aligns with the actual test's core purpose, though this is essentially a coin-flip in terms of expected value.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 102, "outcome": "valid", "prompt_sha256": "c5e7fc4ded08e6e0e34431fd70cbb73825ea978c9c043463a6f1dc31e88b8f0d", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(contaminated | flagged) using Bayes' theorem.\n\n**Setup:**\n- P(contaminated) = 0.10, P(not contaminated) = 0.90\n- P(flagged | contaminated) = 1.00 (never misses)\n- P(flagged | not contaminated) = 0.10 (false positive rate)\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged|contaminated)×P(contaminated) + P(flagged|not contaminated)×P(not contaminated)\nP(flagged) = (1.00)(0.10) + (0.10)(0.90)\nP(flagged) = 0.10 + 0.09 = 0.19\n\n**Calculate posterior probabilities:**\nP(contaminated | flagged) = 0.10 / 0.19 ≈ 0.526\nP(not contaminated | flagged) = 0.09 / 0.19 ≈ 0.474\n\n**Expected values:**\n- Option A: 0.526 × €900 ≈ €474\n- Option B: 0.474 × €1,000 ≈ €474\n\nThese are very close. Let me compute more precisely:\n- Option A: (10/19) × 900 = 9000/19 ≈ €473.68\n- Option B: (9/19) × 1000 = 9000/19 ≈ €473.68\n\n**They are exactly equal!** (10×900 = 9000 = 9×1000)\n\nSince the expected values are identical, either choice is mathematically equivalent. I'll choose Option A, as it aligns with the more likely outcome (contamination is slightly more probable than not, at ~52.6% vs ~47.4%), even though the payouts equalize the expected value.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 103, "outcome": "valid", "prompt_sha256": "d821b23f3efa0436a095e948567c4ac939a2cf5e3aa50a3993887bf8eb9a5dd4", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Let me work through the probabilities first.\n\n**Setup:**\n- 1,000 samples total\n- 100 contaminated → all 100 flagged (no misses)\n- 900 not contaminated → 90 wrongly flagged\n\n**Total flagged:** 100 + 90 = 190\n\n**Given a flag, probability of actual contamination:**\n- P(contaminated | flagged) = 100/190 ≈ 52.6%\n- P(not contaminated | flagged) = 90/190 ≈ 47.4%\n\n**Expected value of each bet:**\n\n*Option A (bet contaminated):*\nEV = (100/190) × €900 + (90/190) × €0\nEV = 0.526 × 900 ≈ €473.68\n\n*Option B (bet not contaminated):*\nEV = (90/190) × €1,000 + (100/190) × €0\nEV = 0.474 × 1,000 ≈ €473.68\n\n**These are essentially equal** (both ≈ €473.68 — in fact, exactly equal: 100×900 = 90,000 and 90×1000 = 90,000).\n\nSince the expected values are mathematically identical, the choice comes down to risk preference — but if forced to pick one, Option A has a *higher probability of winning* (52.6% vs. 47.4%), even though the payout is smaller. For someone who prefers a higher chance of winning something (rather than optimizing purely for expected value, which is tied), Option A is the more natural choice given it aligns with the more probable outcome (contamination is more likely than not, given the flag).\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 104, "outcome": "valid", "prompt_sha256": "f5f9294c8d5bbfe62473d26e93a6f9222871df98fd77e8b9c3f2374efd8ad39d", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to calculate P(mislabeled | flagged) using Bayes' theorem.\n\n**Given information:**\n- P(mislabeled) = 0.08, so P(not mislabeled) = 0.92\n- P(flagged | mislabeled) = 1.00 (never misses)\n- P(flagged | not mislabeled) = 0.25 (false positive rate)\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged | mislabeled) × P(mislabeled) + P(flagged | not mislabeled) × P(not mislabeled)\nP(flagged) = 1.00 × 0.08 + 0.25 × 0.92\nP(flagged) = 0.08 + 0.23 = 0.31\n\n**Calculate posterior probabilities:**\n\nP(mislabeled | flagged) = (1.00 × 0.08) / 0.31 = 0.08 / 0.31 ≈ 0.2581 (25.81%)\n\nP(not mislabeled | flagged) = (0.25 × 0.92) / 0.31 = 0.23 / 0.31 ≈ 0.7419 (74.19%)\n\n**Calculate expected values:**\n\nOption A (bet mislabeled):\nEV(A) = 0.2581 × €2,300 + 0.7419 × €0 ≈ €593.5\n\nOption B (bet not mislabeled):\nEV(B) = 0.7419 × €800 + 0.2581 × €0 ≈ €593.5\n\n**Compare:** These are remarkably close. Let me compute more precisely:\n\nEV(A) = (8/31) × 2300 = 18,400/31 ≈ €593.548\n\nEV(B) = (23/31) × 800 = 18,400/31 ≈ €593.548\n\nThey are **exactly equal**! Both options have identical expected value.\n\nSince the expected values are tied, I'll consider risk: Option B has a much higher probability of winning (74.19% vs 25.81%), offering a smaller but more likely payout — generally preferable for reducing variance.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 105, "outcome": "valid", "prompt_sha256": "8ea1e7045d685ceb58e06b093c52890012ce0dd5e56aa8e9a02160036a0f5fec", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find the probability that a flagged file is actually malicious using Bayes' theorem.\n\n**Given information:**\n- P(malicious) = 0.02, so P(not malicious) = 0.98\n- P(flagged | malicious) = 1.00 (never misses)\n- P(flagged | not malicious) = 0.10 (false positive rate)\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged | malicious) × P(malicious) + P(flagged | not malicious) × P(not malicious)\nP(flagged) = (1.00 × 0.02) + (0.10 × 0.98)\nP(flagged) = 0.02 + 0.098 = 0.118\n\n**Calculate P(malicious | flagged) using Bayes' theorem:**\nP(malicious | flagged) = [P(flagged | malicious) × P(malicious)] / P(flagged)\nP(malicious | flagged) = 0.02 / 0.118 ≈ 0.1695 (about 16.95%)\n\nP(not malicious | flagged) = 1 - 0.1695 ≈ 0.8305 (about 83.05%)\n\n**Calculate expected values:**\n\n**Option A** (bet malicious): \nEV(A) = 0.1695 × €980 + 0.8305 × €0 ≈ €166.10\n\n**Option B** (bet not malicious):\nEV(B) = 0.8305 × €200 + 0.1695 × €0 ≈ €166.10\n\n**Let's be more precise:**\n- P(malicious | flagged) = 0.02/0.118 = 20/118 = 10/59\n- P(not malicious | flagged) = 98/118 = 49/59\n\nEV(A) = (10/59) × 980 = 9800/59 ≈ €166.10\n\nEV(B) = (49/59) × 200 = 9800/59 ≈ €166.10\n\n**The expected values are exactly equal!** This makes sense because the odds ratio (10:49) is designed to offset the payout ratio (980:200 = 49:10).\n\nSince both options yield identical expected value, the choice is mathematically indifferent. However, Option B offers a much higher probability of winning (83% vs 17%), meaning lower variance — a smaller but far more reliable payout. For most decision-makers (especially under typical risk aversion), the safer bet with equal expected value is preferable.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 106, "outcome": "valid", "prompt_sha256": "e264bf8289967baea07bae41b64ee54825a5f01246273e9adf7954773b106cf7", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Given the painting was not flagged, I need P(forged | not flagged).\n\n**Setup:**\n- P(forged) = 0.05, P(not forged) = 0.95\n- P(flagged | forged) = 1.0, so P(not flagged | forged) = 0\n- P(flagged | not forged) = 0.10, so P(not flagged | not forged) = 0.90\n\n**Bayes' theorem:**\n\nSince P(not flagged | forged) = 0, if the painting is forged, it *must* be flagged (the test never misses). So a painting that is **not flagged** cannot be forged.\n\nP(forged | not flagged) = 0\nP(not forged | not flagged) = 1\n\n**Expected values:**\n\n- Option A (bet forged): 0 × €950 = €0\n- Option B (bet not forged): 1 × €500 = €500\n\nSince the test has zero false negatives, a \"not flagged\" result definitively rules out forgery. Betting on forgery would be irrational here — the evidence conclusively points to it being genuine.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 107, "outcome": "valid", "prompt_sha256": "eac363b75e5b78be3cf1611ecab309142865543a7db2a12eaaf336383ce61d43", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(fake | not flagged) and P(not fake | not flagged).\n\n**Setting up base rates:**\n- P(fake) = 0.10, P(not fake) = 0.90\n- P(flagged | fake) = 1.0, so P(not flagged | fake) = 0\n- P(flagged | not fake) = 0.20, so P(not flagged | not fake) = 0.80\n\n**Since the tool never misses a fake review**, any fake review is always flagged. This means if a review was **not flagged**, it cannot be fake.\n\nP(not flagged | fake) = 0, so P(fake | not flagged) = 0.\n\n**Calculating expected values:**\n\nSince the review was not flagged, it is certainly not fake (given the tool's perfect detection of fakes).\n\n- Option A (bet fake): P(fake | not flagged) = 0, so expected value = 0 × €1,800 = €0\n- Option B (bet not fake): P(not fake | not flagged) = 1, so expected value = 1 × €1,000 = €1,000\n\n**Conclusion:** Option B is clearly better — it's a certain win of €1,000, while Option A guarantees €0.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 108, "outcome": "valid", "prompt_sha256": "e5883555311284236af70626b30cdfad0c2a1fd4dbee8f329fd54a45d3ff80e3", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(cracked | flagged) using Bayes' theorem.\n\n**Given information:**\n- P(cracked) = 0.04, so P(not cracked) = 0.96\n- P(flagged | cracked) = 1.00 (never misses)\n- P(flagged | not cracked) = 0.25 (false positive rate)\n\n**Calculate P(flagged):**\n\nP(flagged) = P(flagged | cracked) × P(cracked) + P(flagged | not cracked) × P(not cracked)\n\nP(flagged) = (1.00 × 0.04) + (0.25 × 0.96)\n\nP(flagged) = 0.04 + 0.24 = 0.28\n\n**Calculate posterior probabilities:**\n\nP(cracked | flagged) = 0.04 / 0.28 = 1/7 ≈ 0.1429 (about 14.3%)\n\nP(not cracked | flagged) = 0.24 / 0.28 = 6/7 ≈ 0.8571 (about 85.7%)\n\n**Calculate expected value of each bet:**\n\n**Option A** (bet cracked): \nEV = (1/7) × €2,400 + (6/7) × €0 = €2,400/7 ≈ **€342.86**\n\n**Option B** (bet not cracked):\nEV = (6/7) × €400 + (1/7) × €0 = €2,400/7 ≈ **€342.86**\n\n**Interesting result:** Both bets have identical expected value! The payouts were set exactly to offset the skewed probabilities.\n\n**Tie-breaker consideration:** Since the EVs are mathematically equal, I'd lean toward Option B, because:\n- It has a much higher probability of winning (85.7% vs 14.3%), meaning lower variance\n- For a single one-off bet (not a repeated game), most people prefer the higher-probability, lower-risk option when expected values are tied\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 109, "outcome": "valid", "prompt_sha256": "4379f215a3d749fa4c5444fd9ab31d142eddfc0eebb32ccd7c7f776b57d665de", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Let me calculate the probabilities and expected values.\n\n**Setting up the numbers:**\n- Total files: 1,000\n- Malicious: 20 (all flagged)\n- Non-malicious: 980, of which 98 are wrongly flagged\n\n**Total flagged files:** 20 + 98 = 118\n\n**Probability flagged file is malicious:** 20/118 ≈ 0.1695 (about 17%)\n\n**Probability flagged file is not malicious:** 98/118 ≈ 0.8305 (about 83%)\n\n**Expected value calculations:**\n\nOption A (bet malicious):\nEV = (20/118) × €980 + (98/118) × €0\nEV = 0.1695 × 980 ≈ €166.10\n\nOption B (bet not malicious):\nEV = (98/118) × €200 + (20/118) × €0\nEV = 0.8305 × 200 ≈ €166.10\n\n**Comparing:** Let me be precise with fractions:\n- EV(A) = (20 × 980)/118 = 19,600/118 ≈ €166.10\n- EV(B) = (98 × 200)/118 = 19,600/118 ≈ €166.10\n\nThese are **exactly equal** — both equal 19,600/118 = €166.10.\n\nThis makes sense because the payouts were seemingly designed to offset the base rates: 980×20 = 19,600 and 200×98 = 19,600.\n\nSince the expected values are identical, the choice is a tie in pure EV terms. However, Option B has a much higher probability of winning (83% vs 17%), meaning smaller variance and more consistent payouts — generally preferable if there's any risk aversion, and it also intuitively matches the \"obvious\" fact that a flagged file is more likely a false positive than truly malicious given the low base rate.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 110, "outcome": "valid", "prompt_sha256": "48f611d8dc1dc690e396d0cbc764257f3fdb78ec9dae03ff4f3f9c773595b373", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to calculate P(counterfeit | flagged) using Bayes' theorem.\n\n**Given information:**\n- P(counterfeit) = 0.05\n- P(not counterfeit) = 0.95\n- P(flagged | counterfeit) = 1.00 (never misses)\n- P(flagged | not counterfeit) = 0.02 (false positive rate)\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged | counterfeit) × P(counterfeit) + P(flagged | not counterfeit) × P(not counterfeit)\nP(flagged) = 1.00 × 0.05 + 0.02 × 0.95\nP(flagged) = 0.05 + 0.019 = 0.069\n\n**Calculate P(counterfeit | flagged):**\nP(counterfeit | flagged) = (1.00 × 0.05) / 0.069 = 0.05 / 0.069 ≈ 0.7246\n\n**Calculate P(not counterfeit | flagged):**\nP(not counterfeit | flagged) = 0.019 / 0.069 ≈ 0.2754\n\n**Expected values:**\n\nOption A (bet counterfeit):\nEV(A) = 0.7246 × €190 + 0.2754 × €0 ≈ €137.68\n\nOption B (bet not counterfeit):\nEV(B) = 0.2754 × €500 + 0.7246 × €0 ≈ €137.68\n\n**Comparison:**\nThese are remarkably close. Let me compute more precisely using fractions:\n- P(counterfeit | flagged) = 5/6.9 = 50/69\n- P(not counterfeit | flagged) = 19/69\n\nEV(A) = (50/69) × 190 = 9500/69 ≈ 137.681\nEV(B) = (19/69) × 500 = 9500/69 ≈ 137.681\n\nThe expected values are **exactly equal** (both equal 9500/69 ≈ €137.68). This makes sense: 190×50 = 9500 and 500×19 = 9500.\n\nSince the expected values are identical, either choice is mathematically equivalent. I'll choose Option A, as it aligns with the higher probability event (it's more likely than not that the banknote is actually counterfeit, ~72.5%), which may be preferable if there's any risk aversion consideration or if probabilities are estimated with some uncertainty (favoring the more probable outcome).\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 111, "outcome": "valid", "prompt_sha256": "f6cfb24a5251e1ab4a5c2416cb435249c317e858eb5c4577381773a9631f16fe", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Using Bayes' theorem:\n\n- P(Fraud) = 0.02, P(Not Fraud) = 0.98\n- P(Flag | Fraud) = 1.00\n- P(Flag | Not Fraud) = 0.05\n\nP(Flag) = 0.02×1.00 + 0.98×0.05 = 0.02 + 0.049 = 0.069\n\nP(Fraud | Flag) = 0.02 / 0.069 ≈ 0.2899\nP(Not Fraud | Flag) = 0.049 / 0.069 ≈ 0.7101\n\n**Expected values:**\n\nOption A: 0.2899 × €490 ≈ €142.03\n\nOption B: 0.7101 × €200 ≈ €142.03\n\nThe expected values are essentially equal (the tiny difference is rounding). Let's verify exactly:\n\n- EV(A) = (0.02/0.069) × 490 = 9.8/0.069 ≈ €142.03\n- EV(B) = (0.049/0.069) × 200 = 9.8/0.069 ≈ €142.03\n\nThey're exactly equal — both equal 9.8/0.069. Since the payoffs were essentially calibrated to match the odds, this is a genuine tie in expected value.\n\nGiven the tie, I'll choose Option B, since it has a much higher probability of actually winning (~71% vs ~29%), which is preferable if there's any risk aversion or uncertainty in the stated probabilities.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 112, "outcome": "valid", "prompt_sha256": "3d113eb09425e433c7871a2659023ec3a3b3cf215fe05eccd712bde9688d3bbf", "reason": null, "record_type": "trial", "rep": 0, "response_text": "P(spam) = 0.2, P(not spam) = 0.8\nP(flagged | spam) = 1.0\nP(flagged | not spam) = 0.1\n\nP(flagged) = 0.2×1.0 + 0.8×0.1 = 0.2 + 0.08 = 0.28\n\nP(spam | flagged) = 0.2/0.28 = 5/7 ≈ 0.714\nP(not spam | flagged) = 0.08/0.28 = 2/7 ≈ 0.286\n\nExpected value of Option A: (5/7) × €800 ≈ €571.43\nExpected value of Option B: (2/7) × €2,000 ≈ €571.43\n\nThe expected values are essentially equal (both ≈ €571.43, actually exactly equal: 5×800=4000, 2×2000=4000). This is a genuine tie in expected value.\n\nSince there's no financial advantage either way, I'll go with the option that has the higher probability of winning (lower variance, more likely to actually collect something), which is Option A given the email is far more likely to actually be spam (~71%).\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 113, "outcome": "valid", "prompt_sha256": "8ea1e7045d685ceb58e06b093c52890012ce0dd5e56aa8e9a02160036a0f5fec", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(malicious | flagged) using Bayes' theorem.\n\n**Setup:**\n- P(malicious) = 0.02, P(not malicious) = 0.98\n- P(flagged | malicious) = 1.00\n- P(flagged | not malicious) = 0.10\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged|malicious)×P(malicious) + P(flagged|not malicious)×P(not malicious)\nP(flagged) = 1.00×0.02 + 0.10×0.98\nP(flagged) = 0.02 + 0.098 = 0.118\n\n**Calculate posterior probabilities:**\nP(malicious | flagged) = 0.02 / 0.118 ≈ 0.1695 (16.95%)\nP(not malicious | flagged) = 0.098 / 0.118 ≈ 0.8305 (83.05%)\n\n**Expected values:**\n\nOption A (bet malicious):\nEV(A) = 0.1695 × €980 ≈ €166.10\n\nOption B (bet not malicious):\nEV(B) = 0.8305 × €200 ≈ €166.10\n\n**Check precisely with fractions:**\n- P(malicious|flagged) = 2/118 = 1/59\n- P(not malicious|flagged) = 98/118 = 49/59\n\nEV(A) = (1/59) × 980 = 980/59 ≈ €16.61... \n\nWait, let me recompute: 980/59 ≈ 16.61 — that seems off, let me redo this correctly.\n\nEV(A) = (1/59) × 980 = 16.61 (this is wrong, missing scale)\n\nActually: 980 × (2/118) = 1960/118 ≈ €16.61 — checking arithmetic again: 1960/118 = 16.61... \n\nLet me redo cleanly: 980 × 2 = 1960; 1960/118 = 16.610...\n\nThat doesn't match my earlier calculation. Let me recompute 0.1695 × 980 properly: 0.1695 × 980 = 166.11. And 1960/118 = 16.61 — there's a decimal error. Correct: 1960/118 = 16.610 is wrong; actually 118 × 16 = 1888, remainder 72, so ≈16.61 — but 0.1695×980 should equal this divided by... \n\nI see my error: 1960/118 = 16.61 is incorrect since 118×10=1180, 118×16=1888, 118×16.6=1958.8, so 1960/118 ≈ 16.61 is actually correct, meaning my earlier 166.11 was wrong by a factor of 10.\n\nRecheck: 0.1695 × 980: 0.17×980=166.6, close to 166 confirms **166**, not 16.6. So 1960/118 must equal ~16.6 is inconsistent — resolving: 2/118=0.01695 (not 0.1695!). That's the error — 2/118 = 0.0169.\n\n**Corrected:**\nP(malicious|flagged) = 0.0169 (1.69%)\nP(not malicious|flagged) = 0.9831 (98.31%)\n\nEV(A) = 0.0169 × 980 ≈ €16.61\nEV(B) = 0.9831 × 200 ≈ €196.61\n\n**Option B has much higher expected value.**\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 114, "outcome": "valid", "prompt_sha256": "f504f0527e80201f14754fd04e719d2297904e16b42d1e4c712a93bfdaf03c0c", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I need to find P(cracked | not flagged).\n\n**Setting up the probabilities:**\n- P(cracked) = 0.04, P(not cracked) = 0.96\n- P(flagged | cracked) = 1.00, so P(not flagged | cracked) = 0\n- P(flagged | not cracked) = 0.25, so P(not flagged | not cracked) = 0.75\n\n**Key insight:** Since the scan never misses a cracked weld (sensitivity = 100%), if a weld is cracked, it is *always* flagged. This means P(not flagged | cracked) = 0.\n\nSo if a weld was **not flagged**, it is impossible for it to be cracked.\n\n**Calculating P(cracked | not flagged):**\n\nP(not flagged) = P(not flagged | cracked)·P(cracked) + P(not flagged | not cracked)·P(not cracked)\n= 0 × 0.04 + 0.75 × 0.96\n= 0.72\n\nP(cracked | not flagged) = [P(not flagged | cracked) × P(cracked)] / P(not flagged)\n= (0 × 0.04) / 0.72\n= 0\n\n**Expected values:**\n- Option A: 0 × €2,400 = €0\n- Option B: 1 × €400 = €400\n\nSince the scan has perfect sensitivity, a \"not flagged\" result guarantees the weld is not cracked. Betting on \"cracked\" has zero chance of winning.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 115, "outcome": "valid", "prompt_sha256": "900d0b4a9910fbdfd079980bf7a36fb616de2ba8993846fe6eb60d5c897a08ef", "reason": null, "record_type": "trial", "rep": 0, "response_text": "Total flagged = 100 + 180 = 280\n\nP(fake | flagged) = 100/280 ≈ 0.357\nP(not fake | flagged) = 180/280 ≈ 0.643\n\nExpected value of Option A = 0.357 × €1,800 ≈ €643\nExpected value of Option B = 0.643 × €1,000 ≈ €643\n\nThe two options are essentially equal in expected value (both come out to €900,000/280 ≈ €3,214... let me recompute precisely: 100/280 × 1800 = 180,000/280 ≈ 642.9; 180/280 × 1000 = 180,000/280 ≈ 642.9).\n\nThey're exactly equal — both give the same expected value. With a tie, I'll go with the option that has the higher probability of winning (lower variance, more likely to actually pay out), which is betting it's not fake.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 116, "outcome": "valid", "prompt_sha256": "b355e2ee0cea29b13fc974724dadc3057f5827227b30577c2515eab2c8f60b0d", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Let me calculate the probabilities and expected values.\n\n**Setup:**\n- Total shipments: 1,000\n- Mislabeled: 80 (all flagged)\n- Not mislabeled: 920 (230 wrongly flagged)\n- Total flagged: 80 + 230 = 310\n\n**Probabilities given flagged:**\n- P(mislabeled | flagged) = 80/310 ≈ 25.8%\n- P(not mislabeled | flagged) = 230/310 ≈ 74.2%\n\n**Expected values:**\n\nOption A: EV = (80/310) × €2,300 = €592.26\n\nOption B: EV = (230/310) × €800 = €593.55\n\nThe expected values are very close, with Option B slightly higher (€593.55 vs €592.26).\n\n**Reasoning:** Despite the test seeming reliable (it never misses actual mislabeled shipments), the base rate of mislabeling is low (8%), and the false positive rate (230/920 ≈ 25%) is high enough that most flagged shipments are actually fine. This is a classic base-rate fallacy scenario — the raw \"hit rate\" of the test is misleading without accounting for prior probabilities.\n\nGiven the marginal edge in expected value for Option B, and that it reflects the more statistically likely outcome (the shipment is not actually mislabeled), I'll go with Option B.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 117, "outcome": "valid", "prompt_sha256": "500bc1d28319b0778866a9e86d7a6432fdf2241a406863e4f96fab1c4a623d9b", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(spam | flagged) and P(not spam | flagged) using Bayes' theorem.\n\n**Given information:**\n- P(spam) = 0.20, P(not spam) = 0.80\n- P(flagged | spam) = 1.0 (never misses)\n- P(flagged | not spam) = 0.10 (false positive rate)\n\n**Calculate P(flagged):**\nP(flagged) = P(flagged | spam)·P(spam) + P(flagged | not spam)·P(not spam)\nP(flagged) = (1.0)(0.20) + (0.10)(0.80)\nP(flagged) = 0.20 + 0.08 = 0.28\n\n**Calculate posterior probabilities:**\n\nP(spam | flagged) = [P(flagged | spam)·P(spam)] / P(flagged) = 0.20 / 0.28 = 5/7 ≈ 0.714\n\nP(not spam | flagged) = 0.08 / 0.28 = 2/7 ≈ 0.286\n\n**Calculate expected values:**\n\n**Option A** (bet spam, win €800 if correct):\nEV(A) = (5/7) × €800 + (2/7) × €0 = €4000/7 ≈ €571.43\n\n**Option B** (bet not spam, win €2000 if correct):\nEV(B) = (2/7) × €2000 + (5/7) × €0 = €4000/7 ≈ €571.43\n\n**Comparison:**\nBoth options have identical expected value of €4000/7 ≈ €571.43.\n\nSince the expected values are exactly equal, either choice is mathematically justified. However, Option A carries higher probability of winning (71.4% vs 28.6%), meaning less variance and a higher chance of actually collecting a payout — this makes it the more prudent choice for a typical risk-averse decision-maker, even though the EV is tied.\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 118, "outcome": "valid", "prompt_sha256": "b3260d5e6737bc84c0b2120d489641df765e7dcc912b0c87e1e68b452cf27ff2", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I need to find P(malicious | flagged) using Bayes' theorem.\n\n**Setup:**\n- P(malicious) = 0.02, P(not malicious) = 0.98\n- P(flagged | malicious) = 1.0 (never misses)\n- P(flagged | not malicious) = 0.10 (false positive rate)\n\n**Calculate P(flagged):**\n- P(flagged and malicious) = 0.02 × 1.0 = 0.02\n- P(flagged and not malicious) = 0.98 × 0.10 = 0.098\n- P(flagged) = 0.02 + 0.098 = 0.118\n\n**Calculate posterior probabilities:**\n- P(malicious | flagged) = 0.02 / 0.118 ≈ 0.1695\n- P(not malicious | flagged) = 0.098 / 0.118 ≈ 0.8305\n\n**Calculate expected values:**\n\nOption A (bet malicious): \n- EV = 0.1695 × €980 + 0.8305 × €0 ≈ €166.1\n\nOption B (bet not malicious):\n- EV = 0.8305 × €200 + 0.1695 × €0 ≈ €166.1\n\nThese are remarkably close. Let me compute more precisely using fractions:\n\n- P(malicious | flagged) = 2/118 = 1/59\n- P(not malicious | flagged) = 116/118 = 58/59\n\nEV(A) = (1/59) × 980 = 980/59 ≈ 16.610... ×10 = €166.10\nEV(B) = (58/59) × 200 = 11600/59 ≈ €196.61\n\nLet me redo this calculation:\n- 980/59 = 16.610 → wait, 980/59 ≈ 16.61 — that's wrong scale. Recompute: 59 × 16 = 944, 980-944=36, so 980/59 ≈ 16.61... \n\nThis can't be right since these are euro amounts. Let me recompute: 980/59 ≈ 16.61 is incorrect arithmetic — actually 980/59 = 16.61 is wrong since 59×16=944, remainder 36, so it's 16.6+ — but this should be in the hundreds range. Let me just multiply directly:\n\nEV(A) = 980 × (1/59) = 16.61 **euros**? No: 980 divided by 59 ≈ 16.6 is correct arithmetic, giving **€16.61**? That seems too low.\n\nActually: 980/59 — 59×16=944, 980-944=36, 36/59≈0.61, so 980/59≈16.61. This means EV(A) ≈ €16.61.\n\nEV(B) = 11600/59: 59×196=11564, remainder 36, so ≈196.61. EV(B) ≈ €196.61.\n\n**Comparison:** EV(B) ≈ €196.61 is much greater than EV(A) ≈ €16.61.\n\nOption B has a substantially higher expected value.\n\nDECISION: B", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 119, "outcome": "valid", "prompt_sha256": "76e41241191b29141c4cb6961bf94242322f56d117ddee81c44598b653f0833f", "reason": null, "record_type": "trial", "rep": 1, "response_text": "Out of 1,000 parts: 40 defective (all flagged) + 48 falsely flagged = 88 total flagged.\n\nP(defective | flagged) = 40/88 ≈ 0.4545\nP(not defective | flagged) = 48/88 ≈ 0.5455\n\nExpected value calculations:\n\n**Option A:** 0.4545 × €480 ≈ €218.18\n\n**Option B:** 0.5455 × €400 ≈ €218.18\n\nThe expected values are essentially identical (both equal exactly €2,400/11 ≈ €218.18). This makes sense: the odds (40:48 = 5:6) are inversely proportional to the payouts (480:400 = 6:5), so the bets are perfectly balanced.\n\nSince there's no meaningful difference in expected value, either choice is defensible. I'll go with Option A, betting on defective, as a tie-breaker based on slightly favoring the outcome tied to the camera's perfect detection rate (no false negatives).\n\nDECISION: A", "returned_model_id": "claude-sonnet-5", "run_id": "2026-09-22__base_rate_neglect__wording__v0__claude-sonnet-5__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 0, "outcome": "valid", "prompt_sha256": "2372a54c9defe41dc088ae673cd3f3a1b367d99ad94a6f0831561a98bcd4896f", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__base_rate_neglect__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 1, "outcome": "valid", "prompt_sha256": "f504f0527e80201f14754fd04e719d2297904e16b42d1e4c712a93bfdaf03c0c", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__base_rate_neglect__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "A", "version": "C"}
{"error": null, "index": 2, "outcome": "valid", "prompt_sha256": "201e96927ccbf52a85ff90ccfa3e658eb6b304529e833830a307d285e7d56a3a", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__base_rate_neglect__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 3, "outcome": "valid", "prompt_sha256": "458849e55cf177695845409904c57011c32e8a4bd61d26edb83981d18a26970e", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__base_rate_neglect__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "A", "version": "A"}
{"error": null, "index": 4, "outcome": "valid", "prompt_sha256": "d996562e0bfb516fcfbfbfbaaea6e7d50c4bb17e8f495a702203b7e0211fea56", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__base_rate_neglect__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 5, "outcome": "valid", "prompt_sha256": "c1e82eab26d5df7a9852f1e92f6e1ca68bff0a934ea516423d32faf16988f5bc", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__base_rate_neglect__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 6, "outcome": "valid", "prompt_sha256": "6fdace3427dfdbb2b6d189c0d14078861ed9473eebde135d193c7ce5a3bf2ed3", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__base_rate_neglect__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 7, "outcome": "valid", "prompt_sha256": "f504f0527e80201f14754fd04e719d2297904e16b42d1e4c712a93bfdaf03c0c", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__base_rate_neglect__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "A", "version": "C"}
{"error": null, "index": 8, "outcome": "valid", "prompt_sha256": "eac363b75e5b78be3cf1611ecab309142865543a7db2a12eaaf336383ce61d43", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__base_rate_neglect__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 9, "outcome": "valid", "prompt_sha256": "c5e7fc4ded08e6e0e34431fd70cbb73825ea978c9c043463a6f1dc31e88b8f0d", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__base_rate_neglect__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "A", "version": "A"}
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{"error": null, "index": 3, "outcome": "valid", "prompt_sha256": "3a482d3bb9cc14a151a39e85364b8086815ccc19dd82e2a0bccb9c1897e3d687", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the bee colonies, which is 2/3. Therefore, I would choose Plan B.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 4, "outcome": "valid", "prompt_sha256": "7456b71c362a6775251f56cf83130c772ae0e691c54be63667cc6bb2bdeab731", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 5, "outcome": "unparseable", "prompt_sha256": "5e1719a222df3cef8fbbbaec3016aad6b8eb78fbccec220eaadb49462b17f8b2", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability that all 60 wolves will die is 2/3, which is higher than the 1/2 probability that 40 wolves will die in Plan A. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 6, "outcome": "valid", "prompt_sha256": "af723fc0d14a404f25cdec493fbf6352ff7a6c72b8708b0efe80f512c62c8ac9", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 7, "outcome": "valid", "prompt_sha256": "c1b6e632a60a74bd7dd9a2de8f85c336fd0e99acb3ad8b9c095b71f2fb06e5c2", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 8, "outcome": "valid", "prompt_sha256": "1c563d16650b9a1861b8a918a5fc095fa985bae391c727472ded49e111956d6c", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 9, "outcome": "valid", "prompt_sha256": "3ff43ce8a0d0b488166f6d5c9d24b5394f04826e121c2aee03f85f6a374ab86f", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 10, "outcome": "valid", "prompt_sha256": "6f6a83bf4757e9c741a22d9062334779fa13e40146ed4d3295dd6a37baea92f9", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 11, "outcome": "valid", "prompt_sha256": "3ff43ce8a0d0b488166f6d5c9d24b5394f04826e121c2aee03f85f6a374ab86f", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 12, "outcome": "valid", "prompt_sha256": "787d760c34f474b373009fcd12fecdd425575c516b9d03a7cbb3593eeaa56eb7", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts that will be saved under each plan.\n\n**Plan A:**\n- Number of manuscripts destroyed: 300\n- Number of manuscripts saved: 450 - 300 = 150\n\n**Plan B:**\n- Probability that none of the manuscripts are destroyed: 1/3\n- Probability that all 450 manuscripts are destroyed: 2/3\n\nThe expected number of manuscripts saved under Plan B can be calculated as follows:\n\\[ \\text{Expected number of manuscripts saved} = \\left(\\frac{1}{3} \\times 150\\right) + \\left(\\frac{2}{3} \\times 0\\right) = \\frac{150}{3} = 50 \\]\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 50 manuscripts.\n\nTherefore, Plan A is the better choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 13, "outcome": "valid", "prompt_sha256": "6da92acdf593a53b086a34c5977d4efbb037b5cf15af9fde10624ec223abe322", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 14, "outcome": "unparseable", "prompt_sha256": "6c9079d4c77962ddbb0633c6b4cbd4a96d8847cf97116cfb2eba9f7347056a2c", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "To determine which plan to choose, we need to evaluate the expected number of people saved under each plan.\n\n**Plan A:**\n- Number of people saved: 200\n- Expected number of people saved: 200\n\n**Plan B:**\n- Probability that all 600 people are saved: 1/3\n- Probability that 300 people are saved: 2/3\n- Expected number of people saved: (1/3) * 600 + (2/3) * 300 = 200 + 200 = 400\n\nComparing the two plans:\n- Plan A saves 200 people.\n- Plan B saves 400 people.\n\nTherefore, **DECISION: B**", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 15, "outcome": "valid", "prompt_sha256": "9b9dd3efc0b56a2ca9323c6aaba0383f1fd12292a5b336ef8697568f9932822b", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 16, "outcome": "valid", "prompt_sha256": "f7919b5fc8130327438f795590feb847cd19c3a5a4fe54e4e20fda29d5fa445b", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 17, "outcome": "valid", "prompt_sha256": "babf36e242dfeca98d3a1bed8ca1669e13844b57270674d6ae43074f7efd64bc", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 18, "outcome": "valid", "prompt_sha256": "edc8e86dfb51239157229f508ff6b6d21a8f64e12b98fcbbc34a85c05962d4a1", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 19, "outcome": "valid", "prompt_sha256": "2e86fbbfe9a7b06217fd1acf990117bbdce93a2b153d8090bec775dd36385fc4", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving the most homes, which is 2/3 of 1500 homes, equaling 1000 homes. This is better than Plan A, which saves only 500 homes.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 20, "outcome": "unparseable", "prompt_sha256": "054c24d095fa58ccf7841f14cb51d8b319a8e37d49a6a806f5c63ff1f34df6e0", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of saving the grain. The probability that all 3000 tonnes of grain will be lost is 2/3, which is higher than the 1/3 probability that none of the grain will be lost. Therefore, Plan B has a higher chance of saving the grain. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 21, "outcome": "unparseable", "prompt_sha256": "82d2a53aac7c7424aa923dc583070f8c7d55d730469c3dd9fb9b51dac1e732b0", "reason": "token_not_in_options", "record_type": "trial", "rep": 1, "response_text": "To make a decision, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 110 small businesses will survive.\n- This means 220 small businesses will not survive (330 - 110).\n\n**Plan B:**\n- There is a 1/3 probability that all 330 small businesses will survive.\n- If all 330 small businesses survive, 0 small businesses will not survive.\n- The probability that 165 small businesses will survive is 2/3.\n- If 165 small businesses survive, 165 small businesses will not survive.\n\n**Expected outcomes:**\n- For Plan A: 220 small businesses will not survive.\n- For Plan B: 165 small businesses will not survive.\n\n**Comparison:**\n- Plan A: 220 small businesses will not survive.\n- Plan B: 165 small businesses will not survive.\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 22, "outcome": "valid", "prompt_sha256": "2a61ba6fd082c73c52e19f8a8c6cfa5bd419b2b4a5ec7ee668789bbefad759d5", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 23, "outcome": "unparseable", "prompt_sha256": "5c8abb5e6b883ad75d1f9dc04712057abca573f1acb486179e174bdfd2272ccf", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts saved under each plan.\n\n**Plan A:**\n- 150 manuscripts are saved.\n- Expected number of manuscripts saved = 150 manuscripts.\n\n**Plan B:**\n- There is a 1/3 probability that all 450 manuscripts will be saved.\n- There is a 2/3 probability that 225 of them will be saved.\n- Expected number of manuscripts saved = (1/3 * 450) + (2/3 * 225) = 150 + 150 = 300 manuscripts.\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 300 manuscripts.\n\nTherefore, **DECISION: B** is the better plan.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 24, "outcome": "valid", "prompt_sha256": "1c92977460e44d1f46d69a7aafc171c8668d606223cdd8e43e02eece67d9c1f4", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 25, "outcome": "valid", "prompt_sha256": "50992ee411c24578a6c94c48f18d1f62e237371c3b907df3a7a8163b85f95164", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all 450 manuscripts, which is 2/3. This is better than Plan A, which only saves 150 manuscripts. Therefore, Plan B is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 26, "outcome": "valid", "prompt_sha256": "5c8abb5e6b883ad75d1f9dc04712057abca573f1acb486179e174bdfd2272ccf", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts that will be saved under each plan.\n\n**Plan A:**\n- 150 manuscripts are saved.\n- Expected number of manuscripts saved = 150 manuscripts.\n\n**Plan B:**\n- There is a 1/3 probability that all 450 manuscripts will be saved.\n- There is a 2/3 probability that 225 of them will be saved.\n- Expected number of manuscripts saved = (1/3 * 450) + (2/3 * 225) = 150 + 150 = 300 manuscripts.\n\nComparing the two plans:\n- Plan A: 150 manuscripts saved.\n- Plan B: 300 manuscripts saved.\n\nSince 300 manuscripts is more than 150 manuscripts, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 27, "outcome": "valid", "prompt_sha256": "7ce37b109a6117554cafe49f766780cfddce7aecf48ae4e189f014067b8fd91a", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To make a decision, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- This means 1/3 * 1200 = 400 hectares are saved.\n- There is a 2/3 probability that none of the 1200 hectares will be saved.\n- This means 2/3 * 1200 = 800 hectares are at risk.\n\n**Expected outcome for Plan A:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\n**Expected outcome for Plan B:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\nBoth plans result in 800 hectares at risk. However, Plan A is more certain (100% certainty that 400 hectares are saved), while Plan B has a 1/3 chance of saving all the forest and a 2/3 chance of saving none.\n\nGiven the certainty of Plan A, it is likely to be the preferred choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 28, "outcome": "unparseable", "prompt_sha256": "4d8e93c9e42a796c0ac1cc83878390f80c0fe9783ffdbe06acc522e3d8a8ecbc", "reason": "token_not_in_options", "record_type": "trial", "rep": 0, "response_text": "To make an informed decision, let's evaluate both plans:\n\n**Plan A:**\n- 1000 tonnes of grain are saved.\n- This plan saves 1000 tonnes out of 3000 tonnes at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 3000 tonnes will be saved.\n- There is a 2/3 probability that 1500 tonnes will be saved.\n\nLet's calculate the expected amount of grain saved under each plan:\n\n**Plan A:**\n- Expected grain saved = 1000 tonnes\n\n**Plan B:**\n- Expected grain saved = (1/3) * 3000 tonnes + (2/3) * 1500 tonnes\n- Expected grain saved = 1000 tonnes + 1000 tonnes\n- Expected grain saved = 2000 tonnes\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 29, "outcome": "valid", "prompt_sha256": "1a8cc04152b268084d2d2d31450895e813ec316543b604932ba8a71e12d85120", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 30, "outcome": "valid", "prompt_sha256": "3a2812b1f6ff45945aba3a1f8fe040678be464b9257e87217477718df27aae89", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of all 60 wolves surviving is 2/3, which is greater than the 1/2 probability of 20 wolves surviving in Plan A.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 31, "outcome": "unparseable", "prompt_sha256": "054c24d095fa58ccf7841f14cb51d8b319a8e37d49a6a806f5c63ff1f34df6e0", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "I choose Plan B because it has a higher probability of saving the grain. The probability that all 3000 tonnes of grain will be lost is 2/3, which is higher than the 1/3 probability that none of the grain will be lost. Therefore, Plan B has a higher chance of saving the grain. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 32, "outcome": "unparseable", "prompt_sha256": "971ed7f2dce2ec386643f1b74bb6490221ec7cfd67f97fd65d50746c5a2dbe1e", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "To make an informed decision, let's analyze the two plans:\n\n**Plan A** involves losing 600 jobs out of 900. This means 300 jobs will be saved.\n\n**Plan B** involves a 1/3 probability of losing none of the jobs and a 2/3 probability of losing all the jobs. This means the probability of saving 300 jobs is 2/3.\n\nComparing the probabilities:\n- Plan A: 300 jobs saved with a probability of 100%.\n- Plan B: 300 jobs saved with a probability of 2/3.\n\nSince the probability of saving 300 jobs is higher in Plan B, it is more likely that 300 jobs will be saved under Plan B.\n\nTherefore, DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 33, "outcome": "unparseable", "prompt_sha256": "5a98c9b275f2e269f441136545c26e9517f770e42f2ac6673cafb36d6b5b3dee", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of saving all 60 wolves is 2/3, which is greater than the 1/2 probability of saving 20 wolves in Plan A. Therefore, Plan B is the better choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 34, "outcome": "unparseable", "prompt_sha256": "04c15b15999a53d35fb4e65beeed26b1493898927f2f31ba89ad00ad3bda146a", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of recovering all 2400 customer accounts. The probability of recovering all accounts is 2/3, which is greater than the 1/2 probability of recovering 800 accounts in Plan A. Therefore, Plan B has a higher chance of success and is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 35, "outcome": "valid", "prompt_sha256": "d0671648f97b8cae3a3993c8036a30d1fed2b0ba872a5db8b7fb3d0691a0a88c", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 36, "outcome": "valid", "prompt_sha256": "c1b6e632a60a74bd7dd9a2de8f85c336fd0e99acb3ad8b9c095b71f2fb06e5c2", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 37, "outcome": "valid", "prompt_sha256": "2f12a26dd43f83eb1aeec2cbb75f6c45ab39b64f72c1ff2a52d562de4172cf3b", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 38, "outcome": "valid", "prompt_sha256": "3a2812b1f6ff45945aba3a1f8fe040678be464b9257e87217477718df27aae89", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of all 60 wolves surviving is 2/3, which is greater than the 1/2 probability of 20 wolves surviving in Plan A.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 39, "outcome": "unparseable", "prompt_sha256": "82d2a53aac7c7424aa923dc583070f8c7d55d730469c3dd9fb9b51dac1e732b0", "reason": "token_not_in_options", "record_type": "trial", "rep": 0, "response_text": "To make a decision, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 110 small businesses will survive.\n- This means 220 small businesses will not survive (330 - 110).\n\n**Plan B:**\n- There is a 1/3 probability that all 330 small businesses will survive.\n- If all 330 small businesses survive, 0 small businesses will not survive.\n- The probability that 165 small businesses will survive is 2/3.\n- If 165 small businesses survive, 165 small businesses will not survive.\n\n**Expected outcomes:**\n- For Plan A: 220 small businesses will not survive.\n- For Plan B: 165 small businesses will not survive.\n\n**Comparison:**\n- Plan A: 220 small businesses will not survive.\n- Plan B: 165 small businesses will not survive.\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 40, "outcome": "valid", "prompt_sha256": "0254fe148f9d0ebce727a3cf45400f5dcf858e9fa4dc7b7508c7c2f69c2e18b9", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nPlan B has a higher probability of ensuring that no children fall ill, which is 2/3. This is a safer option given the risk of contamination.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 41, "outcome": "valid", "prompt_sha256": "b1f4ebde4bb72094296c6a1945bef8947aecd7f37a7d5dfae89f3f2f664a190e", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 42, "outcome": "valid", "prompt_sha256": "65cf4426caa5639fb56501a173e81174c3fc807466503a072ddb294e0d56cea0", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. Therefore, it is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 43, "outcome": "valid", "prompt_sha256": "44af3fe999f4069a756676e5f18745d3dc53dd88bf5c50c4c235fe7d216f285e", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 44, "outcome": "valid", "prompt_sha256": "b2b47b9f50a439165a0bca2338d37d0fb0d4c74a7cb5641a188b0e2e121c2fa8", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B\n\nPlan B has a higher probability of not losing any containers, which is 2/3. This means there is a 1/3 chance that all 450 containers will be lost, which is a higher risk than Plan A where 300 containers are lost. Therefore, Plan B is the safer option.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 45, "outcome": "valid", "prompt_sha256": "2dffb1f6a6f42ac6e5d87674b0c1ff9267e851757704187f43f69e107ac2fb7b", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. This means there is a 1/3 chance that none of the grain will be saved, which is a risk that Plan A does not have. Therefore, Plan B is the safer option.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 46, "outcome": "valid", "prompt_sha256": "65cf4426caa5639fb56501a173e81174c3fc807466503a072ddb294e0d56cea0", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. Therefore, it is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 47, "outcome": "valid", "prompt_sha256": "2a61ba6fd082c73c52e19f8a8c6cfa5bd419b2b4a5ec7ee668789bbefad759d5", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 48, "outcome": "valid", "prompt_sha256": "af723fc0d14a404f25cdec493fbf6352ff7a6c72b8708b0efe80f512c62c8ac9", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 49, "outcome": "unparseable", "prompt_sha256": "b860798b39d8d9d7b3c62b818127346fd97fdab93864c7b0e2265bc4aaaec381", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of saving all the forest. The probability of saving all 1200 hectares is 2/3, which is greater than the 1/2 probability of saving 400 hectares in Plan A. Therefore, Plan B is more likely to save the entire forest. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 50, "outcome": "unparseable", "prompt_sha256": "283907a9d79322a05d7786e0caa70a2b10f75ea5f75f7066226e4df3601ff261", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which could be beneficial for the town's economy. However, I also consider the potential risk of all businesses failing, which could lead to significant economic losses. The decision should be based on a thorough analysis of the potential outcomes and the town's priorities. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A_prime"}
{"error": null, "index": 51, "outcome": "valid", "prompt_sha256": "202a72fcce05b6df32805d3c650b65e25a970be250588a4703903a18e9a26854", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 52, "outcome": "unparseable", "prompt_sha256": "4d8e93c9e42a796c0ac1cc83878390f80c0fe9783ffdbe06acc522e3d8a8ecbc", "reason": "token_not_in_options", "record_type": "trial", "rep": 1, "response_text": "To make an informed decision, let's evaluate both plans:\n\n**Plan A:**\n- 1000 tonnes of grain are saved.\n- This plan saves 1000 tonnes out of 3000 tonnes at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 3000 tonnes will be saved.\n- There is a 2/3 probability that 1500 tonnes will be saved.\n\nLet's calculate the expected amount of grain saved under each plan:\n\n**Plan A:**\n- Expected grain saved = 1000 tonnes\n\n**Plan B:**\n- Expected grain saved = (1/3) * 3000 tonnes + (2/3) * 1500 tonnes\n- Expected grain saved = 1000 tonnes + 1000 tonnes\n- Expected grain saved = 2000 tonnes\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 53, "outcome": "valid", "prompt_sha256": "48e4375c003683cb043d91bb17a4199165510c73f521c156070a8a36ac45b2f8", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 54, "outcome": "valid", "prompt_sha256": "6b679de71f8b792fd776a5f20b33d5942ada39e2bd68023b782f7336f5574be3", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To determine which plan to choose, we need to calculate the expected number of containers that will be recovered under each plan.\n\n**Plan A:**\n- Number of containers recovered: 150\n- Expected number of containers recovered: 150\n\n**Plan B:**\n- Probability that all 450 containers are recovered: 1/3\n- Probability that 225 containers are recovered: 2/3\n- Expected number of containers recovered: (1/3) * 450 + (2/3) * 225 = 150 + 150 = 300\n\nComparing the expected values:\n- Plan A: 150 containers\n- Plan B: 300 containers\n\nSince 300 containers is greater than 150 containers, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 55, "outcome": "valid", "prompt_sha256": "9e1c48da2e125cc47b13ec2d681224d329a8498e633e051f2b7f808a5794cbc9", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To determine which plan is better, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk (1200 - 400).\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- There is a 2/3 probability that 600 hectares will be saved.\n\nLet's calculate the expected number of hectares saved in Plan B:\n\n- Probability of saving all 1200 hectares: \\( \\frac{1}{3} \\)\n- Probability of saving 600 hectares: \\( \\frac{2}{3} \\)\n\nExpected number of hectares saved = \\( \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\)\n\nNow, let's calculate this:\n\n\\[ \\text{Expected number of hectares saved} = \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\]\n\\[ = 400 + 400 \\]\n\\[ = 800 \\]\n\nSo, the expected number of hectares saved in Plan B is 800.\n\n**Comparison:**\n- Plan A saves 400 hectares.\n- Plan B saves 800 hectares.\n\nSince 800 hectares is more than 400 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 56, "outcome": "valid", "prompt_sha256": "609f6b15c5f091890109f1ed87acb262a66c9c066ef107a40ecb8ad1b2708e81", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To determine which plan is better, we need to calculate the expected number of surviving bee colonies under each plan.\n\n**Plan A:**\n- 400 bee colonies will survive.\n- Expected number of surviving colonies = 400.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 bee colonies will survive.\n- There is a 2/3 probability that 600 of them will survive.\n- Expected number of surviving colonies = (1/3 * 1200) + (2/3 * 600) = 400 + 400 = 800.\n\nComparing the two plans:\n- Plan A: 400 surviving colonies.\n- Plan B: 800 surviving colonies.\n\nSince 800 is greater than 400, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 57, "outcome": "valid", "prompt_sha256": "0037a5d0a4c92f3aee2d28d516a4da0148dd3d079dfe7254d8ae2fc30cbb8d49", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 58, "outcome": "valid", "prompt_sha256": "0037a5d0a4c92f3aee2d28d516a4da0148dd3d079dfe7254d8ae2fc30cbb8d49", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 59, "outcome": "unparseable", "prompt_sha256": "14569a8f52b1d4fdcf54b3cce7058f6302ed0351ba7ab4af83a221e778a2c69d", "reason": "token_not_in_options", "record_type": "trial", "rep": 1, "response_text": "To determine which plan is better, we need to calculate the expected number of small businesses that will close under each plan.\n\n**Plan A:**\n- If 220 small businesses close, the expected number of small businesses that will close is 220.\n\n**Plan B:**\n- The probability that none of the 330 small businesses will close is \\( \\frac{1}{3} \\).\n- The probability that all 330 small businesses will close is \\( \\frac{2}{3} \\).\n- The expected number of small businesses that will close is the sum of the probabilities of each scenario:\n  \\[\n  \\text{Expected number of small businesses closing} = \\left(\\frac{1}{3} \\times 0\\right) + \\left(\\frac{2}{3} \\times 330\\right) = 0 + 220 = 220\n  \\]\n\nBoth plans result in the same expected number of small businesses closing, which is 220. Therefore, either plan A or plan B would be equally effective in terms of the expected number of small businesses that will close.\n\nDECISION: A or B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 60, "outcome": "unparseable", "prompt_sha256": "08a12d85b996707087fe56ef0fb7217cd591c867e1943fe6754f073776f78515", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of saving the most people. With a 2/3 probability that all 600 people will die, it is more likely that fewer people will die compared to Plan A where 400 people will die. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 61, "outcome": "valid", "prompt_sha256": "62e70abd9ad24812b47da8f7592c7ab8cb6d390ccf8c3d93bbc80870a3d5dc42", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 62, "outcome": "valid", "prompt_sha256": "9e9e3283093d3563304522958f60061c120a343b538e2458c276ace96937356d", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To make an informed decision, let's evaluate both plans:\n\n**Plan A:**\n- 800 customer accounts will be recovered.\n- This plan guarantees that 800 accounts are recovered, but it does not provide any information about the recovery of the remaining 1600 accounts.\n\n**Plan B:**\n- There is a 1/3 probability that all 2400 customer accounts will be recovered.\n- There is a 2/3 probability that 1200 of them will be recovered.\n- This plan has a higher probability of recovering all 2400 accounts, which is 2/3.\n\nGiven the goal is to recover as many customer accounts as possible, **Plan B** is the better choice because it has a higher probability of recovering all 2400 accounts.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 63, "outcome": "valid", "prompt_sha256": "202a72fcce05b6df32805d3c650b65e25a970be250588a4703903a18e9a26854", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 64, "outcome": "unparseable", "prompt_sha256": "6c9079d4c77962ddbb0633c6b4cbd4a96d8847cf97116cfb2eba9f7347056a2c", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "To determine which plan to choose, we need to evaluate the expected number of people saved under each plan.\n\n**Plan A:**\n- Number of people saved: 200\n- Expected number of people saved: 200\n\n**Plan B:**\n- Probability that all 600 people are saved: 1/3\n- Probability that 300 people are saved: 2/3\n- Expected number of people saved: (1/3) * 600 + (2/3) * 300 = 200 + 200 = 400\n\nComparing the two plans:\n- Plan A saves 200 people.\n- Plan B saves 400 people.\n\nTherefore, **DECISION: B**", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 65, "outcome": "valid", "prompt_sha256": "6f6a83bf4757e9c741a22d9062334779fa13e40146ed4d3295dd6a37baea92f9", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 66, "outcome": "valid", "prompt_sha256": "f7919b5fc8130327438f795590feb847cd19c3a5a4fe54e4e20fda29d5fa445b", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 67, "outcome": "unparseable", "prompt_sha256": "97d449dc071c07d4c0dbc5edfaa5003a341a490e5d490a47bdb1a7e6ef0d6f91", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which is 2/3 or approximately 66.67%. This means that there is a higher chance that all the businesses will be able to recover from the earthquake damage.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 68, "outcome": "valid", "prompt_sha256": "b860798b39d8d9d7b3c62b818127346fd97fdab93864c7b0e2265bc4aaaec381", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To make a decision, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- This means 1/3 * 1200 = 400 hectares are saved.\n- There is a 2/3 probability that none of the 1200 hectares will be saved.\n- This means 2/3 * 1200 = 800 hectares are at risk.\n\n**Expected outcomes:**\n- Plan A: 800 hectares at risk.\n- Plan B: 800 hectares at risk.\n\nBoth plans result in 800 hectares at risk. Since both plans are equally likely to result in the same outcome, either plan can be chosen. However, if we consider the probability of each plan being chosen, Plan B has a higher probability of being chosen (2/3).\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 69, "outcome": "valid", "prompt_sha256": "48e4375c003683cb043d91bb17a4199165510c73f521c156070a8a36ac45b2f8", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 70, "outcome": "valid", "prompt_sha256": "9e9e3283093d3563304522958f60061c120a343b538e2458c276ace96937356d", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To make an informed decision, let's evaluate both plans:\n\n**Plan A:**\n- 800 customer accounts will be recovered.\n- This plan guarantees that 800 accounts are recovered, but it does not provide any information about the recovery of the remaining 1600 accounts.\n\n**Plan B:**\n- There is a 1/3 probability that all 2400 customer accounts will be recovered.\n- There is a 2/3 probability that 1200 of them will be recovered.\n- This plan has a higher probability of recovering all 2400 accounts, which is 2/3.\n\nGiven the goal is to recover as many customer accounts as possible, **Plan B** is the better choice because it has a higher probability of recovering all 2400 accounts.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 71, "outcome": "valid", "prompt_sha256": "5a8846970ae24c83fc47585a39ed1f97828f5475f6e761190adef24e34259087", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To determine which plan is better, we need to calculate the expected number of bee colonies that will die under each plan.\n\n**Plan A:**\n- 800 bee colonies will die.\n- Expected number of bee colonies that will die = 800.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 bee colonies will die.\n- There is a 2/3 probability that all 1200 bee colonies will die.\n- Expected number of bee colonies that will die = (1/3) * 0 + (2/3) * 1200 = 2400.\n\nComparing the two plans:\n- Plan A: 800 bee colonies will die.\n- Plan B: 2400 bee colonies will die.\n\nSince 2400 is greater than 800, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 72, "outcome": "unparseable", "prompt_sha256": "04c15b15999a53d35fb4e65beeed26b1493898927f2f31ba89ad00ad3bda146a", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "I choose Plan B because it has a higher probability of recovering all 2400 customer accounts. The probability of recovering all accounts is 2/3, which is greater than the 1/2 probability of recovering 800 accounts in Plan A. Therefore, Plan B has a higher chance of success and is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 73, "outcome": "valid", "prompt_sha256": "f779bb83f28de5af7bd94b1d13bd6ffa3609db7e4c12d8aac2f55443d4d40d05", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To determine which plan is better, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 800 hectares of forest will burn.\n- This means 400 hectares of forest will not burn.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 hectares of forest will burn.\n- There is a 2/3 probability that all 1200 hectares of forest will burn.\n\nLet's calculate the expected area burned under each plan:\n\n**Plan A:**\n- 800 hectares will burn.\n- 400 hectares will not burn.\n- Total area burned = 800 hectares.\n\n**Plan B:**\n- Probability of no burn = 1/3\n- Probability of all burn = 2/3\n- Expected area burned = (1/3) * 1200 + (2/3) * 1200\n- Expected area burned = 400 + 800\n- Expected area burned = 1200 hectares.\n\nComparing the two plans:\n- Plan A: 800 hectares burned\n- Plan B: 1200 hectares burned\n\nSince 1200 hectares is more than 800 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 74, "outcome": "valid", "prompt_sha256": "6b679de71f8b792fd776a5f20b33d5942ada39e2bd68023b782f7336f5574be3", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To determine which plan to choose, we need to calculate the expected number of containers that will be recovered under each plan.\n\n**Plan A:**\n- Number of containers recovered: 150\n- Expected number of containers recovered: 150\n\n**Plan B:**\n- Probability that all 450 containers are recovered: 1/3\n- Probability that 225 containers are recovered: 2/3\n- Expected number of containers recovered: (1/3) * 450 + (2/3) * 225 = 150 + 150 = 300\n\nComparing the expected values:\n- Plan A: 150 containers\n- Plan B: 300 containers\n\nSince 300 containers is greater than 150 containers, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 75, "outcome": "valid", "prompt_sha256": "2dffb1f6a6f42ac6e5d87674b0c1ff9267e851757704187f43f69e107ac2fb7b", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. This means there is a 1/3 chance that none of the grain will be saved, which is a risk that Plan A does not have. Therefore, Plan B is the safer option.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 76, "outcome": "valid", "prompt_sha256": "1da1ca378b35a1935d70110b76cb1a815ed98c1201a4114170324085cf772f2c", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B\n\nPlan B is chosen because it has a higher probability of saving the most homes, with a 2/3 probability that all 1500 homes will be saved.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 77, "outcome": "valid", "prompt_sha256": "3a482d3bb9cc14a151a39e85364b8086815ccc19dd82e2a0bccb9c1897e3d687", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the bee colonies, which is 2/3. Therefore, I would choose Plan B.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 78, "outcome": "valid", "prompt_sha256": "61de0674f34c6486b832f34789da59450032b069498e736fd4f98399eef14ec4", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 79, "outcome": "valid", "prompt_sha256": "50992ee411c24578a6c94c48f18d1f62e237371c3b907df3a7a8163b85f95164", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all 450 manuscripts, which is 2/3. This is better than Plan A, which only saves 150 manuscripts. Therefore, Plan B is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 80, "outcome": "unparseable", "prompt_sha256": "5e1719a222df3cef8fbbbaec3016aad6b8eb78fbccec220eaadb49462b17f8b2", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability that all 60 wolves will die is 2/3, which is higher than the 1/2 probability that 40 wolves will die in Plan A. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 81, "outcome": "unparseable", "prompt_sha256": "5a98c9b275f2e269f441136545c26e9517f770e42f2ac6673cafb36d6b5b3dee", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of saving all 60 wolves is 2/3, which is greater than the 1/2 probability of saving 20 wolves in Plan A. Therefore, Plan B is the better choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 82, "outcome": "valid", "prompt_sha256": "b2b47b9f50a439165a0bca2338d37d0fb0d4c74a7cb5641a188b0e2e121c2fa8", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nPlan B has a higher probability of not losing any containers, which is 2/3. This means there is a 1/3 chance that all 450 containers will be lost, which is a higher risk than Plan A where 300 containers are lost. Therefore, Plan B is the safer option.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 83, "outcome": "valid", "prompt_sha256": "f779bb83f28de5af7bd94b1d13bd6ffa3609db7e4c12d8aac2f55443d4d40d05", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To determine which plan is better, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 800 hectares of forest will burn.\n- This means 400 hectares of forest will not burn.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 hectares of forest will burn.\n- There is a 2/3 probability that all 1200 hectares of forest will burn.\n\nLet's calculate the expected area burned under each plan:\n\n**Plan A:**\n- 800 hectares will burn.\n- 400 hectares will not burn.\n- Total area burned = 800 hectares.\n\n**Plan B:**\n- Probability of no burn = 1/3\n- Probability of all burn = 2/3\n- Expected area burned = (1/3) * 1200 + (2/3) * 1200\n- Expected area burned = 400 + 800\n- Expected area burned = 1200 hectares.\n\nComparing the two plans:\n- Plan A: 800 hectares burned\n- Plan B: 1200 hectares burned\n\nSince 1200 hectares is more than 800 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 84, "outcome": "unparseable", "prompt_sha256": "283907a9d79322a05d7786e0caa70a2b10f75ea5f75f7066226e4df3601ff261", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which could be beneficial for the town's economy. However, I also consider the potential risk of all businesses failing, which could lead to significant economic losses. The decision should be based on a thorough analysis of the potential outcomes and the town's priorities. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A_prime"}
{"error": null, "index": 85, "outcome": "valid", "prompt_sha256": "787d760c34f474b373009fcd12fecdd425575c516b9d03a7cbb3593eeaa56eb7", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts that will be saved under each plan.\n\n**Plan A:**\n- Number of manuscripts destroyed: 300\n- Number of manuscripts saved: 450 - 300 = 150\n\n**Plan B:**\n- Probability that none of the manuscripts are destroyed: 1/3\n- Probability that all 450 manuscripts are destroyed: 2/3\n\nThe expected number of manuscripts saved under Plan B can be calculated as follows:\n\\[ \\text{Expected number of manuscripts saved} = \\left(\\frac{1}{3} \\times 0\\right) + \\left(\\frac{2}{3} \\times 450\\right) = 0 + \\frac{900}{3} = 300 \\]\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 300 manuscripts.\n\nTherefore, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 86, "outcome": "valid", "prompt_sha256": "b558642e1cbc215a0d6ee27711872ff8da30bbaff5fb318a952158b4ee6e44df", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To make an informed decision, let's analyze the probabilities and potential outcomes for each plan:\n\n**Plan A:**\n- 1600 customer accounts will be lost.\n- This means 800 accounts will be saved.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 2400 customer accounts will be lost.\n- If this happens, 2400 accounts will be saved.\n- There is a 2/3 probability that all 2400 customer accounts will be lost.\n- If this happens, 0 accounts will be saved.\n\n**Analysis:**\n- Plan A guarantees 800 accounts will be saved.\n- Plan B has a 2/3 chance of saving all 2400 accounts, which is the best outcome.\n\n**Conclusion:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 87, "outcome": "valid", "prompt_sha256": "edc8e86dfb51239157229f508ff6b6d21a8f64e12b98fcbbc34a85c05962d4a1", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 88, "outcome": "valid", "prompt_sha256": "d0671648f97b8cae3a3993c8036a30d1fed2b0ba872a5db8b7fb3d0691a0a88c", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 89, "outcome": "valid", "prompt_sha256": "44af3fe999f4069a756676e5f18745d3dc53dd88bf5c50c4c235fe7d216f285e", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 90, "outcome": "valid", "prompt_sha256": "2f12a26dd43f83eb1aeec2cbb75f6c45ab39b64f72c1ff2a52d562de4172cf3b", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 91, "outcome": "unparseable", "prompt_sha256": "97d449dc071c07d4c0dbc5edfaa5003a341a490e5d490a47bdb1a7e6ef0d6f91", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which is 2/3 or approximately 66.67%. This means that there is a higher chance that all the businesses will be able to recover from the earthquake damage.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 92, "outcome": "valid", "prompt_sha256": "62e70abd9ad24812b47da8f7592c7ab8cb6d390ccf8c3d93bbc80870a3d5dc42", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 93, "outcome": "valid", "prompt_sha256": "9e1c48da2e125cc47b13ec2d681224d329a8498e633e051f2b7f808a5794cbc9", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To determine which plan is better, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk (1200 - 400).\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- There is a 2/3 probability that 600 hectares will be saved.\n\nLet's calculate the expected number of hectares saved in Plan B:\n\n- Probability of saving all 1200 hectares: \\( \\frac{1}{3} \\)\n- Probability of saving 600 hectares: \\( \\frac{2}{3} \\)\n\nExpected number of hectares saved = \\( \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\)\n\nNow, let's calculate this:\n\n\\[ \\text{Expected number of hectares saved} = \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\]\n\\[ = 400 + 400 \\]\n\\[ = 800 \\]\n\nSo, the expected number of hectares saved in Plan B is 800.\n\n**Comparison:**\n- Plan A saves 400 hectares.\n- Plan B saves 800 hectares.\n\nSince 800 hectares is more than 400 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 94, "outcome": "valid", "prompt_sha256": "82a919ffdd0850c8641a7e1e27cdf001c0b687f39af014f286b0dca1d1684579", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving all 600 people, which is 2/3. While Plan A saves 200 people, it is not guaranteed to save everyone, and the probability of saving all 600 people is only 1/3.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 95, "outcome": "valid", "prompt_sha256": "a1dfb76cbdbfc30ab5f7074bb5a16e5c206fcfae5f38e7ba2e0f7704c3ef8fc0", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 96, "outcome": "valid", "prompt_sha256": "3dac32627ae92008819ace85add0277dab846de18ba623aeb3d24b690ab398a2", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 97, "outcome": "valid", "prompt_sha256": "3dac32627ae92008819ace85add0277dab846de18ba623aeb3d24b690ab398a2", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 98, "outcome": "valid", "prompt_sha256": "a1dfb76cbdbfc30ab5f7074bb5a16e5c206fcfae5f38e7ba2e0f7704c3ef8fc0", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 99, "outcome": "valid", "prompt_sha256": "61de0674f34c6486b832f34789da59450032b069498e736fd4f98399eef14ec4", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 100, "outcome": "valid", "prompt_sha256": "5a8846970ae24c83fc47585a39ed1f97828f5475f6e761190adef24e34259087", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To determine which plan is better, we need to calculate the expected number of bee colonies that will die under each plan.\n\n**Plan A:**\n- 800 bee colonies will die.\n- Expected number of bee colonies that will die = 800.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 bee colonies will die.\n- There is a 2/3 probability that all 1200 bee colonies will die.\n- Expected number of bee colonies that will die = (1/3) * 0 + (2/3) * 1200 = 2400.\n\nComparing the two plans:\n- Plan A: 800 bee colonies will die.\n- Plan B: 2400 bee colonies will die.\n\nSince 2400 is greater than 800, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 101, "outcome": "valid", "prompt_sha256": "6da92acdf593a53b086a34c5977d4efbb037b5cf15af9fde10624ec223abe322", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 102, "outcome": "valid", "prompt_sha256": "82a919ffdd0850c8641a7e1e27cdf001c0b687f39af014f286b0dca1d1684579", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving all 600 people, which is 2/3. While Plan A saves 200 people, it is not guaranteed to save everyone, and the probability of saving all 600 people is only 1/3.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 103, "outcome": "valid", "prompt_sha256": "1da1ca378b35a1935d70110b76cb1a815ed98c1201a4114170324085cf772f2c", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nPlan B is chosen because it has a higher probability of saving the most homes, with a 2/3 probability that all 1500 homes will be saved.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 104, "outcome": "valid", "prompt_sha256": "609f6b15c5f091890109f1ed87acb262a66c9c066ef107a40ecb8ad1b2708e81", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To determine which plan is better, we need to calculate the expected number of surviving bee colonies under each plan.\n\n**Plan A:**\n- 400 bee colonies will survive.\n- Expected number of surviving colonies = 400.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 bee colonies will survive.\n- There is a 2/3 probability that 600 of them will survive.\n- Expected number of surviving colonies = (1/3 * 1200) + (2/3 * 600) = 400 + 400 = 800.\n\nComparing the two plans:\n- Plan A: 400 surviving colonies.\n- Plan B: 800 surviving colonies.\n\nSince 800 is greater than 400, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 105, "outcome": "valid", "prompt_sha256": "b558642e1cbc215a0d6ee27711872ff8da30bbaff5fb318a952158b4ee6e44df", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To make an informed decision, let's analyze the probabilities and potential outcomes for each plan:\n\n**Plan A:**\n- 1600 customer accounts will be lost.\n- This means 800 accounts will be saved.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 2400 customer accounts will be lost.\n- If this happens, 2400 accounts will be saved.\n- There is a 2/3 probability that all 2400 customer accounts will be lost.\n- If this happens, 0 accounts will be saved.\n\n**Analysis:**\n- Plan A guarantees 800 accounts will be saved.\n- Plan B has a 2/3 chance of saving all 2400 accounts, which is the best outcome.\n\n**Conclusion:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 106, "outcome": "valid", "prompt_sha256": "9b9dd3efc0b56a2ca9323c6aaba0383f1fd12292a5b336ef8697568f9932822b", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 107, "outcome": "valid", "prompt_sha256": "babf36e242dfeca98d3a1bed8ca1669e13844b57270674d6ae43074f7efd64bc", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 108, "outcome": "valid", "prompt_sha256": "1c563d16650b9a1861b8a918a5fc095fa985bae391c727472ded49e111956d6c", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 109, "outcome": "valid", "prompt_sha256": "2e86fbbfe9a7b06217fd1acf990117bbdce93a2b153d8090bec775dd36385fc4", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving the most homes, which is 2/3 of 1500 homes, equaling 1000 homes. This is better than Plan A, which saves only 500 homes.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 110, "outcome": "valid", "prompt_sha256": "2515d3452ef63d4c84da44d45d127c0b304a79651c8a63c9b037ecb151f7e661", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 111, "outcome": "unparseable", "prompt_sha256": "14569a8f52b1d4fdcf54b3cce7058f6302ed0351ba7ab4af83a221e778a2c69d", "reason": "token_not_in_options", "record_type": "trial", "rep": 0, "response_text": "To determine which plan is better, we need to calculate the expected number of small businesses that will close under each plan.\n\n**Plan A:**\n- If 220 small businesses close, the expected number of small businesses that will close is 220.\n\n**Plan B:**\n- The probability that none of the 330 small businesses will close is \\( \\frac{1}{3} \\).\n- The probability that all 330 small businesses will close is \\( \\frac{2}{3} \\).\n- The expected number of small businesses that will close is the sum of the probabilities of each scenario:\n  \\[\n  \\text{Expected number of small businesses closing} = \\left(\\frac{1}{3} \\times 0\\right) + \\left(\\frac{2}{3} \\times 330\\right) = 0 + 220 = 220\n  \\]\n\nBoth plans result in the same expected number of small businesses closing, which is 220. Therefore, either plan A or plan B would be equally effective in terms of the expected number of small businesses that will close.\n\nDECISION: A or B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 112, "outcome": "unparseable", "prompt_sha256": "971ed7f2dce2ec386643f1b74bb6490221ec7cfd67f97fd65d50746c5a2dbe1e", "reason": "token_not_in_options", "record_type": "trial", "rep": 1, "response_text": "To make an informed decision, let's analyze the two plans:\n\n**Plan A:** 600 jobs will be lost.\n**Plan B:** There is a 1/3 probability that none of the 900 jobs will be lost, and a 2/3 probability that all 900 jobs will be lost.\n\n**Analysis:**\n- Plan A results in the loss of 600 jobs.\n- Plan B results in the loss of either 0 or 900 jobs, with probabilities of 1/3 and 2/3 respectively.\n\n**Decision:**\nGiven the potential outcomes, Plan B has a higher probability of not losing any jobs (2/3 chance), which is more favorable than losing 600 jobs (Plan A).\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 113, "outcome": "valid", "prompt_sha256": "1c92977460e44d1f46d69a7aafc171c8668d606223cdd8e43e02eece67d9c1f4", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 114, "outcome": "valid", "prompt_sha256": "1a8cc04152b268084d2d2d31450895e813ec316543b604932ba8a71e12d85120", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 115, "outcome": "valid", "prompt_sha256": "f061b5fa3edd89e2dd4fdeb8d9fd317564f8710838dc7bf7da7f41532b4b552e", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving all 900 jobs, which is 2/3. This means there is a 66.67% chance that all 900 jobs will be saved, compared to 30% chance with Plan A.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 116, "outcome": "valid", "prompt_sha256": "2515d3452ef63d4c84da44d45d127c0b304a79651c8a63c9b037ecb151f7e661", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 117, "outcome": "valid", "prompt_sha256": "7ce37b109a6117554cafe49f766780cfddce7aecf48ae4e189f014067b8fd91a", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To make a decision, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- This means 1/3 * 1200 = 400 hectares are saved.\n- There is a 2/3 probability that none of the 1200 hectares will be saved.\n- This means 2/3 * 1200 = 800 hectares are at risk.\n\n**Expected outcome for Plan A:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\n**Expected outcome for Plan B:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\nBoth plans result in 800 hectares at risk. However, Plan A is more certain (100% certainty that 400 hectares are saved), while Plan B has a 1/3 chance of saving all the forest and a 2/3 chance of saving none.\n\nGiven the certainty of Plan A, it is likely to be the preferred choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 118, "outcome": "unparseable", "prompt_sha256": "08a12d85b996707087fe56ef0fb7217cd591c867e1943fe6754f073776f78515", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "I choose Plan B because it has a higher probability of saving the most people. With a 2/3 probability that all 600 people will die, it is more likely that fewer people will die compared to Plan A where 400 people will die. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 119, "outcome": "valid", "prompt_sha256": "7456b71c362a6775251f56cf83130c772ae0e691c54be63667cc6bb2bdeab731", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v0__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 0, "outcome": "valid", "prompt_sha256": "3dac32627ae92008819ace85add0277dab846de18ba623aeb3d24b690ab398a2", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 1, "outcome": "valid", "prompt_sha256": "b2b47b9f50a439165a0bca2338d37d0fb0d4c74a7cb5641a188b0e2e121c2fa8", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B\n\nPlan B has a higher probability of not losing any containers, which is 2/3. This means there is a 1/3 chance that all 450 containers will be lost, which is a higher risk than Plan A where 300 containers are lost. Therefore, Plan B is the safer option.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 2, "outcome": "valid", "prompt_sha256": "44af3fe999f4069a756676e5f18745d3dc53dd88bf5c50c4c235fe7d216f285e", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 3, "outcome": "valid", "prompt_sha256": "2f12a26dd43f83eb1aeec2cbb75f6c45ab39b64f72c1ff2a52d562de4172cf3b", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 4, "outcome": "valid", "prompt_sha256": "b558642e1cbc215a0d6ee27711872ff8da30bbaff5fb318a952158b4ee6e44df", "reason": null, "record_type": "trial", "rep": 7, "response_text": "To make an informed decision, let's analyze the probabilities and potential outcomes for each plan:\n\n**Plan A:**\n- 1600 customer accounts will be lost.\n- This means 800 accounts will be saved.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 2400 customer accounts will be lost.\n- If this happens, 2400 accounts will be saved.\n- There is a 2/3 probability that all 2400 customer accounts will be lost.\n- If this happens, 0 accounts will be saved.\n\n**Analysis:**\n- Plan A guarantees 800 accounts will be saved.\n- Plan B has a 2/3 chance of saving all 2400 accounts, which is the best outcome.\n\n**Conclusion:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 5, "outcome": "valid", "prompt_sha256": "1a8cc04152b268084d2d2d31450895e813ec316543b604932ba8a71e12d85120", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 6, "outcome": "unparseable", "prompt_sha256": "283907a9d79322a05d7786e0caa70a2b10f75ea5f75f7066226e4df3601ff261", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which could be beneficial for the town's economy. However, I also consider the potential risk of all businesses failing, which could lead to significant economic losses. The decision should be based on a thorough analysis of the potential outcomes and the town's priorities. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A_prime"}
{"error": null, "index": 7, "outcome": "valid", "prompt_sha256": "61de0674f34c6486b832f34789da59450032b069498e736fd4f98399eef14ec4", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 8, "outcome": "unparseable", "prompt_sha256": "5a98c9b275f2e269f441136545c26e9517f770e42f2ac6673cafb36d6b5b3dee", "reason": "no_decision_line", "record_type": "trial", "rep": 7, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of saving all 60 wolves is 2/3, which is greater than the 1/2 probability of saving 20 wolves in Plan A. Therefore, Plan B is the better choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 9, "outcome": "valid", "prompt_sha256": "48e4375c003683cb043d91bb17a4199165510c73f521c156070a8a36ac45b2f8", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 10, "outcome": "valid", "prompt_sha256": "f779bb83f28de5af7bd94b1d13bd6ffa3609db7e4c12d8aac2f55443d4d40d05", "reason": null, "record_type": "trial", "rep": 3, "response_text": "To determine which plan is better, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 800 hectares of forest will burn.\n- This means 400 hectares of forest will not burn.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 hectares of forest will burn.\n- There is a 2/3 probability that all 1200 hectares of forest will burn.\n\nLet's calculate the expected area burned under each plan:\n\n**Plan A:**\n- 800 hectares will burn.\n- 400 hectares will not burn.\n- Total area burned = 800 hectares.\n\n**Plan B:**\n- Probability of no burn = 1/3\n- Probability of all burn = 2/3\n- Expected area burned = (1/3) * 1200 + (2/3) * 1200\n- Expected area burned = 400 + 800\n- Expected area burned = 1200 hectares.\n\nComparing the two plans:\n- Plan A: 800 hectares burned\n- Plan B: 1200 hectares burned\n\nSince 1200 hectares is more than 800 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 11, "outcome": "unparseable", "prompt_sha256": "14569a8f52b1d4fdcf54b3cce7058f6302ed0351ba7ab4af83a221e778a2c69d", "reason": "no_decision_line", "record_type": "trial", "rep": 6, "response_text": "To determine which plan is better, we need to calculate the expected number of small businesses that will close under each plan.\n\n**Plan A:**\n- If 220 small businesses close, the expected number of small businesses that will close is 220.\n\n**Plan B:**\n- The probability that none of the 330 small businesses will close is \\( \\frac{1}{3} \\).\n- The probability that all 330 small businesses will close is \\( \\frac{2}{3} \\).\n- The expected number of small businesses that will close is the sum of the probabilities of each scenario:\n  \\[\n  \\text{Expected number of small businesses closing} = \\left(\\frac{1}{3} \\times 0\\right) + \\left(\\frac{2}{3} \\times 330\\right) = 0 + 220 = 220\n  \\]\n\nBoth plans result in the same expected number of small businesses closing, which is 220. Therefore, either plan A or plan B would be equally effective in terms of the expected number of small businesses that will close.\n\nDECISION: A or B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 12, "outcome": "valid", "prompt_sha256": "2dffb1f6a6f42ac6e5d87674b0c1ff9267e851757704187f43f69e107ac2fb7b", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. This means there is a 1/3 chance that none of the grain will be saved, which is a risk that Plan A does not have. Therefore, Plan B is the safer choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 13, "outcome": "valid", "prompt_sha256": "f061b5fa3edd89e2dd4fdeb8d9fd317564f8710838dc7bf7da7f41532b4b552e", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving all 900 jobs, which is 2/3. This means there is a 66.67% chance that all 900 jobs will be saved, compared to 30% chance with Plan A.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 14, "outcome": "valid", "prompt_sha256": "babf36e242dfeca98d3a1bed8ca1669e13844b57270674d6ae43074f7efd64bc", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 15, "outcome": "valid", "prompt_sha256": "1c92977460e44d1f46d69a7aafc171c8668d606223cdd8e43e02eece67d9c1f4", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 16, "outcome": "valid", "prompt_sha256": "1c563d16650b9a1861b8a918a5fc095fa985bae391c727472ded49e111956d6c", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 17, "outcome": "valid", "prompt_sha256": "4d8e93c9e42a796c0ac1cc83878390f80c0fe9783ffdbe06acc522e3d8a8ecbc", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To make an informed decision, let's evaluate both plans:\n\n**Plan A:**\n- 1000 tonnes of grain are saved.\n- This plan saves 1000 tonnes out of 3000 tonnes at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 3000 tonnes will be saved.\n- There is a 2/3 probability that 1500 tonnes will be saved.\n\nLet's calculate the expected amount of grain saved under each plan:\n\n**Plan A:**\n- Expected grain saved = 1000 tonnes\n\n**Plan B:**\n- Expected grain saved = (1/3) * 3000 tonnes + (2/3) * 1500 tonnes\n- Expected grain saved = 1000 tonnes + 1000 tonnes\n- Expected grain saved = 2000 tonnes\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 18, "outcome": "valid", "prompt_sha256": "0254fe148f9d0ebce727a3cf45400f5dcf858e9fa4dc7b7508c7c2f69c2e18b9", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B\n\nPlan B has a higher probability of ensuring that no children fall ill, which is 2/3. This is a safer option given the risk of contamination.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 19, "outcome": "valid", "prompt_sha256": "af723fc0d14a404f25cdec493fbf6352ff7a6c72b8708b0efe80f512c62c8ac9", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 20, "outcome": "valid", "prompt_sha256": "202a72fcce05b6df32805d3c650b65e25a970be250588a4703903a18e9a26854", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 21, "outcome": "valid", "prompt_sha256": "6f6a83bf4757e9c741a22d9062334779fa13e40146ed4d3295dd6a37baea92f9", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 22, "outcome": "valid", "prompt_sha256": "9b9dd3efc0b56a2ca9323c6aaba0383f1fd12292a5b336ef8697568f9932822b", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 23, "outcome": "valid", "prompt_sha256": "7456b71c362a6775251f56cf83130c772ae0e691c54be63667cc6bb2bdeab731", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 24, "outcome": "valid", "prompt_sha256": "af723fc0d14a404f25cdec493fbf6352ff7a6c72b8708b0efe80f512c62c8ac9", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 25, "outcome": "valid", "prompt_sha256": "f779bb83f28de5af7bd94b1d13bd6ffa3609db7e4c12d8aac2f55443d4d40d05", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To determine which plan is better, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 800 hectares of forest will burn.\n- This means 400 hectares of forest will not burn.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 hectares of forest will burn.\n- There is a 2/3 probability that all 1200 hectares of forest will burn.\n\nLet's calculate the expected area burned under each plan:\n\n**Plan A:**\n- 800 hectares will burn.\n- 400 hectares will not burn.\n- Total area burned = 800 hectares.\n\n**Plan B:**\n- Probability of no burn = 1/3\n- Probability of all burn = 2/3\n- Expected area burned = (1/3) * 1200 + (2/3) * 1200\n- Expected area burned = 400 + 800\n- Expected area burned = 1200 hectares.\n\nComparing the two plans:\n- Plan A: 800 hectares burned\n- Plan B: 1200 hectares burned\n\nSince 1200 hectares is more than 800 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 26, "outcome": "unparseable", "prompt_sha256": "14569a8f52b1d4fdcf54b3cce7058f6302ed0351ba7ab4af83a221e778a2c69d", "reason": "no_decision_line", "record_type": "trial", "rep": 2, "response_text": "To determine which plan is better, we need to calculate the expected number of small businesses that will close under each plan.\n\n**Plan A:**\n- If 220 small businesses close, the expected number of small businesses that will close is 220.\n\n**Plan B:**\n- The probability that none of the 330 small businesses will close is \\( \\frac{1}{3} \\).\n- The probability that all 330 small businesses will close is \\( \\frac{2}{3} \\).\n- The expected number of small businesses that will close is the sum of the probabilities of each scenario:\n  \\[\n  \\text{Expected number of small businesses closing} = \\left(\\frac{1}{3} \\times 0\\right) + \\left(\\frac{2}{3} \\times 330\\right) = 0 + 220 = 220\n  \\]\n\nBoth plans result in the same expected number of small businesses closing, which is 220. Therefore, either plan A or plan B would be equally effective in terms of the expected number of small businesses that will close.\n\nDECISION: A or B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 27, "outcome": "valid", "prompt_sha256": "0037a5d0a4c92f3aee2d28d516a4da0148dd3d079dfe7254d8ae2fc30cbb8d49", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 28, "outcome": "valid", "prompt_sha256": "50992ee411c24578a6c94c48f18d1f62e237371c3b907df3a7a8163b85f95164", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all 450 manuscripts, which is 2/3. This is better than Plan A, which only saves 150 manuscripts. Therefore, Plan B is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 29, "outcome": "valid", "prompt_sha256": "65cf4426caa5639fb56501a173e81174c3fc807466503a072ddb294e0d56cea0", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. Therefore, it is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 30, "outcome": "valid", "prompt_sha256": "2515d3452ef63d4c84da44d45d127c0b304a79651c8a63c9b037ecb151f7e661", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 31, "outcome": "valid", "prompt_sha256": "1a8cc04152b268084d2d2d31450895e813ec316543b604932ba8a71e12d85120", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 32, "outcome": "valid", "prompt_sha256": "6f6a83bf4757e9c741a22d9062334779fa13e40146ed4d3295dd6a37baea92f9", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 33, "outcome": "valid", "prompt_sha256": "babf36e242dfeca98d3a1bed8ca1669e13844b57270674d6ae43074f7efd64bc", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 34, "outcome": "valid", "prompt_sha256": "d0671648f97b8cae3a3993c8036a30d1fed2b0ba872a5db8b7fb3d0691a0a88c", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 35, "outcome": "unparseable", "prompt_sha256": "283907a9d79322a05d7786e0caa70a2b10f75ea5f75f7066226e4df3601ff261", "reason": "no_decision_line", "record_type": "trial", "rep": 4, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which could be beneficial for the town's economy. However, I also consider the potential risk of all businesses failing, which could lead to significant economic losses. The decision should be based on a thorough analysis of the potential outcomes and the town's priorities. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A_prime"}
{"error": null, "index": 36, "outcome": "valid", "prompt_sha256": "7456b71c362a6775251f56cf83130c772ae0e691c54be63667cc6bb2bdeab731", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 37, "outcome": "valid", "prompt_sha256": "b2b47b9f50a439165a0bca2338d37d0fb0d4c74a7cb5641a188b0e2e121c2fa8", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the containers, which is 2/3. Therefore, I would choose Plan B.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 38, "outcome": "valid", "prompt_sha256": "2f12a26dd43f83eb1aeec2cbb75f6c45ab39b64f72c1ff2a52d562de4172cf3b", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 39, "outcome": "valid", "prompt_sha256": "f779bb83f28de5af7bd94b1d13bd6ffa3609db7e4c12d8aac2f55443d4d40d05", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To determine which plan is better, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 800 hectares of forest will burn.\n- This means 400 hectares of forest will not burn.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 hectares of forest will burn.\n- There is a 2/3 probability that all 1200 hectares of forest will burn.\n\nLet's calculate the expected area burned under each plan:\n\n**Plan A:**\n- 800 hectares will burn.\n- 400 hectares will not burn.\n- Total area burned = 800 hectares.\n\n**Plan B:**\n- Probability of no burn = 1/3\n- Probability of all burn = 2/3\n- Expected area burned = (1/3) * 1200 + (2/3) * 1200\n- Expected area burned = 400 + 800\n- Expected area burned = 1200 hectares.\n\nComparing the two plans:\n- Plan A: 800 hectares burned\n- Plan B: 1200 hectares burned\n\nSince 1200 hectares is more than 800 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 40, "outcome": "valid", "prompt_sha256": "48e4375c003683cb043d91bb17a4199165510c73f521c156070a8a36ac45b2f8", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 41, "outcome": "unparseable", "prompt_sha256": "5e1719a222df3cef8fbbbaec3016aad6b8eb78fbccec220eaadb49462b17f8b2", "reason": "no_decision_line", "record_type": "trial", "rep": 6, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability that all 60 wolves will die is 2/3, which is higher than the 1/2 probability that 40 wolves will die in Plan A. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 42, "outcome": "valid", "prompt_sha256": "1c92977460e44d1f46d69a7aafc171c8668d606223cdd8e43e02eece67d9c1f4", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 43, "outcome": "valid", "prompt_sha256": "2515d3452ef63d4c84da44d45d127c0b304a79651c8a63c9b037ecb151f7e661", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 44, "outcome": "valid", "prompt_sha256": "2f12a26dd43f83eb1aeec2cbb75f6c45ab39b64f72c1ff2a52d562de4172cf3b", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 45, "outcome": "valid", "prompt_sha256": "2a61ba6fd082c73c52e19f8a8c6cfa5bd419b2b4a5ec7ee668789bbefad759d5", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 46, "outcome": "valid", "prompt_sha256": "4d8e93c9e42a796c0ac1cc83878390f80c0fe9783ffdbe06acc522e3d8a8ecbc", "reason": null, "record_type": "trial", "rep": 4, "response_text": "To make an informed decision, let's evaluate both plans:\n\n**Plan A:**\n- 1000 tonnes of grain are saved.\n- This plan saves 1000 tonnes out of 3000 tonnes at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 3000 tonnes will be saved.\n- There is a 2/3 probability that 1500 tonnes will be saved.\n\nLet's calculate the expected amount of grain saved under each plan:\n\n**Plan A:**\n- Expected grain saved = 1000 tonnes\n\n**Plan B:**\n- Expected grain saved = (1/3) * 3000 tonnes + (2/3) * 1500 tonnes\n- Expected grain saved = 1000 tonnes + 1000 tonnes\n- Expected grain saved = 2000 tonnes\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 47, "outcome": "valid", "prompt_sha256": "1c563d16650b9a1861b8a918a5fc095fa985bae391c727472ded49e111956d6c", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 48, "outcome": "valid", "prompt_sha256": "b558642e1cbc215a0d6ee27711872ff8da30bbaff5fb318a952158b4ee6e44df", "reason": null, "record_type": "trial", "rep": 3, "response_text": "To make an informed decision, let's analyze the probabilities and potential outcomes for each plan:\n\n**Plan A:**\n- 1600 customer accounts will be lost.\n- This means 800 accounts will be saved.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 2400 customer accounts will be lost.\n- If this happens, 2400 accounts will be saved.\n- There is a 2/3 probability that all 2400 customer accounts will be lost.\n- If this happens, 0 accounts will be saved.\n\n**Analysis:**\n- Plan A guarantees 800 accounts will be saved.\n- Plan B has a 2/3 chance of saving all 2400 accounts, which is the best outcome.\n\n**Conclusion:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 49, "outcome": "valid", "prompt_sha256": "787d760c34f474b373009fcd12fecdd425575c516b9d03a7cbb3593eeaa56eb7", "reason": null, "record_type": "trial", "rep": 7, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts that will be saved under each plan.\n\n**Plan A:**\n- Number of manuscripts destroyed: 300\n- Number of manuscripts saved: 450 - 300 = 150\n\n**Plan B:**\n- Probability that none of the manuscripts are destroyed: 1/3\n- Probability that all 450 manuscripts are destroyed: 2/3\n\nThe expected number of manuscripts saved under Plan B can be calculated as follows:\n\\[ \\text{Expected number of manuscripts saved} = \\left(\\frac{1}{3} \\times 150\\right) + \\left(\\frac{2}{3} \\times 0\\right) = \\frac{150}{3} = 50 \\]\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 50 manuscripts.\n\nTherefore, Plan A is the better choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 50, "outcome": "valid", "prompt_sha256": "4d8e93c9e42a796c0ac1cc83878390f80c0fe9783ffdbe06acc522e3d8a8ecbc", "reason": null, "record_type": "trial", "rep": 5, "response_text": "To make an informed decision, let's evaluate both plans:\n\n**Plan A:**\n- 1000 tonnes of grain are saved.\n- This plan saves 1000 tonnes out of 3000 tonnes at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 3000 tonnes will be saved.\n- There is a 2/3 probability that 1500 tonnes will be saved.\n\nLet's calculate the expected amount of grain saved under each plan:\n\n**Plan A:**\n- Expected grain saved = 1000 tonnes\n\n**Plan B:**\n- Expected grain saved = (1/3) * 3000 tonnes + (2/3) * 1500 tonnes\n- Expected grain saved = 1000 tonnes + 1000 tonnes\n- Expected grain saved = 2000 tonnes\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 51, "outcome": "valid", "prompt_sha256": "2a61ba6fd082c73c52e19f8a8c6cfa5bd419b2b4a5ec7ee668789bbefad759d5", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 52, "outcome": "valid", "prompt_sha256": "3a482d3bb9cc14a151a39e85364b8086815ccc19dd82e2a0bccb9c1897e3d687", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the bee colonies, which is 2/3. Therefore, I would choose Plan B.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 53, "outcome": "valid", "prompt_sha256": "c1b6e632a60a74bd7dd9a2de8f85c336fd0e99acb3ad8b9c095b71f2fb06e5c2", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 54, "outcome": "valid", "prompt_sha256": "f061b5fa3edd89e2dd4fdeb8d9fd317564f8710838dc7bf7da7f41532b4b552e", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving all 900 jobs, which is 2/3. This means there is a 66.67% chance that all 900 jobs will be saved, compared to 30% chance with Plan A.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 55, "outcome": "valid", "prompt_sha256": "65cf4426caa5639fb56501a173e81174c3fc807466503a072ddb294e0d56cea0", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. Therefore, it is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 56, "outcome": "valid", "prompt_sha256": "5a8846970ae24c83fc47585a39ed1f97828f5475f6e761190adef24e34259087", "reason": null, "record_type": "trial", "rep": 6, "response_text": "To determine which plan is better, we need to calculate the expected number of bee colonies that will die under each plan.\n\n**Plan A:**\n- 800 bee colonies will die.\n- Expected number of bee colonies that will die = 800.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 bee colonies will die.\n- There is a 2/3 probability that all 1200 bee colonies will die.\n- Expected number of bee colonies that will die = (1/3) * 0 + (2/3) * 1200 = 2400.\n\nComparing the two plans:\n- Plan A: 800 bee colonies will die.\n- Plan B: 2400 bee colonies will die.\n\nSince 2400 is greater than 800, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 57, "outcome": "unparseable", "prompt_sha256": "b860798b39d8d9d7b3c62b818127346fd97fdab93864c7b0e2265bc4aaaec381", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of saving all the forest. The probability of saving all 1200 hectares is 2/3, which is greater than the 1/2 probability of saving 400 hectares in Plan A. Therefore, Plan B is more likely to save the entire forest. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 58, "outcome": "valid", "prompt_sha256": "1c92977460e44d1f46d69a7aafc171c8668d606223cdd8e43e02eece67d9c1f4", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 59, "outcome": "valid", "prompt_sha256": "4d8e93c9e42a796c0ac1cc83878390f80c0fe9783ffdbe06acc522e3d8a8ecbc", "reason": null, "record_type": "trial", "rep": 2, "response_text": "To make an informed decision, let's evaluate both plans:\n\n**Plan A:**\n- 1000 tonnes of grain are saved.\n- This plan saves 1000 tonnes out of 3000 tonnes at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 3000 tonnes will be saved.\n- There is a 2/3 probability that 1500 tonnes will be saved.\n\nLet's calculate the expected amount of grain saved under each plan:\n\n**Plan A:**\n- Expected grain saved = 1000 tonnes\n\n**Plan B:**\n- Expected grain saved = (1/3) * 3000 tonnes + (2/3) * 1500 tonnes\n- Expected grain saved = 1000 tonnes + 1000 tonnes\n- Expected grain saved = 2000 tonnes\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 60, "outcome": "valid", "prompt_sha256": "a1dfb76cbdbfc30ab5f7074bb5a16e5c206fcfae5f38e7ba2e0f7704c3ef8fc0", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 61, "outcome": "valid", "prompt_sha256": "3dac32627ae92008819ace85add0277dab846de18ba623aeb3d24b690ab398a2", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 62, "outcome": "valid", "prompt_sha256": "62e70abd9ad24812b47da8f7592c7ab8cb6d390ccf8c3d93bbc80870a3d5dc42", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 63, "outcome": "valid", "prompt_sha256": "3a2812b1f6ff45945aba3a1f8fe040678be464b9257e87217477718df27aae89", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of all 60 wolves surviving is 2/3, which is greater than the 1/2 probability of 20 wolves surviving in Plan A.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 64, "outcome": "valid", "prompt_sha256": "d0671648f97b8cae3a3993c8036a30d1fed2b0ba872a5db8b7fb3d0691a0a88c", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 65, "outcome": "valid", "prompt_sha256": "2dffb1f6a6f42ac6e5d87674b0c1ff9267e851757704187f43f69e107ac2fb7b", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. This means there is a 1/3 chance that none of the grain will be saved, which is a risk that Plan A does not have. Therefore, Plan B is the safer option.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 66, "outcome": "valid", "prompt_sha256": "48e4375c003683cb043d91bb17a4199165510c73f521c156070a8a36ac45b2f8", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 67, "outcome": "valid", "prompt_sha256": "9e1c48da2e125cc47b13ec2d681224d329a8498e633e051f2b7f808a5794cbc9", "reason": null, "record_type": "trial", "rep": 4, "response_text": "To determine which plan is better, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk (1200 - 400).\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- There is a 2/3 probability that 600 hectares will be saved.\n\nLet's calculate the expected number of hectares saved in Plan B:\n\n- Probability of saving all 1200 hectares: \\( \\frac{1}{3} \\)\n- Probability of saving 600 hectares: \\( \\frac{2}{3} \\)\n\nExpected number of hectares saved = \\( \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\)\n\nNow, let's calculate this:\n\n\\[ \\text{Expected number of hectares saved} = \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\]\n\\[ = 400 + 400 \\]\n\\[ = 800 \\]\n\nSo, the expected number of hectares saved in Plan B is 800.\n\n**Comparison:**\n- Plan A saves 400 hectares.\n- Plan B saves 800 hectares.\n\nSince 800 hectares is more than 400 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 68, "outcome": "valid", "prompt_sha256": "2dffb1f6a6f42ac6e5d87674b0c1ff9267e851757704187f43f69e107ac2fb7b", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. This means there is a 1/3 chance that none of the grain will be saved, which is a risk that Plan A does not have. Therefore, Plan B is the safer option.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 69, "outcome": "valid", "prompt_sha256": "62e70abd9ad24812b47da8f7592c7ab8cb6d390ccf8c3d93bbc80870a3d5dc42", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 70, "outcome": "unparseable", "prompt_sha256": "97d449dc071c07d4c0dbc5edfaa5003a341a490e5d490a47bdb1a7e6ef0d6f91", "reason": "no_decision_line", "record_type": "trial", "rep": 4, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which is 2/3 or approximately 66.67%. This means that there is a higher chance that all the businesses will be able to recover from the earthquake damage.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 71, "outcome": "valid", "prompt_sha256": "1c563d16650b9a1861b8a918a5fc095fa985bae391c727472ded49e111956d6c", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 72, "outcome": "valid", "prompt_sha256": "6b679de71f8b792fd776a5f20b33d5942ada39e2bd68023b782f7336f5574be3", "reason": null, "record_type": "trial", "rep": 7, "response_text": "To determine which plan to choose, we need to calculate the expected number of containers that will be recovered under each plan.\n\n**Plan A:**\n- Number of containers recovered: 150\n- Expected number of containers recovered: 150\n\n**Plan B:**\n- Probability that all 450 containers are recovered: 1/3\n- Probability that 225 containers are recovered: 2/3\n- Expected number of containers recovered: (1/3) * 450 + (2/3) * 225 = 150 + 150 = 300\n\nComparing the expected values:\n- Plan A: 150 containers\n- Plan B: 300 containers\n\nSince 300 containers is greater than 150 containers, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 73, "outcome": "valid", "prompt_sha256": "2dffb1f6a6f42ac6e5d87674b0c1ff9267e851757704187f43f69e107ac2fb7b", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. This means there is a 1/3 chance that none of the grain will be saved, which is a risk that Plan A does not have. Therefore, Plan B is the safer option.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 74, "outcome": "valid", "prompt_sha256": "1a8cc04152b268084d2d2d31450895e813ec316543b604932ba8a71e12d85120", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 75, "outcome": "valid", "prompt_sha256": "f7919b5fc8130327438f795590feb847cd19c3a5a4fe54e4e20fda29d5fa445b", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 76, "outcome": "valid", "prompt_sha256": "edc8e86dfb51239157229f508ff6b6d21a8f64e12b98fcbbc34a85c05962d4a1", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 77, "outcome": "valid", "prompt_sha256": "c1b6e632a60a74bd7dd9a2de8f85c336fd0e99acb3ad8b9c095b71f2fb06e5c2", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 78, "outcome": "valid", "prompt_sha256": "f7919b5fc8130327438f795590feb847cd19c3a5a4fe54e4e20fda29d5fa445b", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 79, "outcome": "valid", "prompt_sha256": "f779bb83f28de5af7bd94b1d13bd6ffa3609db7e4c12d8aac2f55443d4d40d05", "reason": null, "record_type": "trial", "rep": 7, "response_text": "To determine which plan is better, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 800 hectares of forest will burn.\n- This means 400 hectares of forest will not burn.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 hectares of forest will burn.\n- There is a 2/3 probability that all 1200 hectares of forest will burn.\n\nLet's calculate the expected area burned under each plan:\n\n**Plan A:**\n- 800 hectares will burn.\n- 400 hectares will not burn.\n- Total area burned = 800 hectares.\n\n**Plan B:**\n- Probability of no burn = 1/3\n- Probability of all burn = 2/3\n- Expected area burned = (1/3) * 1200 + (2/3) * 1200\n- Expected area burned = 400 + 800\n- Expected area burned = 1200 hectares.\n\nComparing the two plans:\n- Plan A: 800 hectares burned\n- Plan B: 1200 hectares burned\n\nSince 1200 hectares is more than 800 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 80, "outcome": "valid", "prompt_sha256": "edc8e86dfb51239157229f508ff6b6d21a8f64e12b98fcbbc34a85c05962d4a1", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 81, "outcome": "unparseable", "prompt_sha256": "14569a8f52b1d4fdcf54b3cce7058f6302ed0351ba7ab4af83a221e778a2c69d", "reason": "no_decision_line", "record_type": "trial", "rep": 7, "response_text": "To determine which plan is better, we need to calculate the expected number of small businesses that will close under each plan.\n\n**Plan A:**\n- If 220 small businesses close, the expected number of small businesses that will close is 220.\n\n**Plan B:**\n- The probability that none of the 330 small businesses will close is \\( \\frac{1}{3} \\).\n- The probability that all 330 small businesses will close is \\( \\frac{2}{3} \\).\n- The expected number of small businesses that will close is the sum of the probabilities of each scenario:\n  \\[\n  \\text{Expected number of small businesses closing} = \\left(\\frac{1}{3} \\times 0\\right) + \\left(\\frac{2}{3} \\times 330\\right) = 0 + 220 = 220\n  \\]\n\nBoth plans result in the same expected number of small businesses closing, which is 220. Therefore, either plan A or plan B would be equally effective in terms of the expected number of small businesses that will close.\n\nDECISION: A or B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 82, "outcome": "unparseable", "prompt_sha256": "f061b5fa3edd89e2dd4fdeb8d9fd317564f8710838dc7bf7da7f41532b4b552e", "reason": "no_decision_line", "record_type": "trial", "rep": 5, "response_text": "To make an informed decision, let's analyze both plans:\n\n**Plan A**:\n- 300 jobs are saved.\n- This plan saves 300 jobs out of 900 at risk.\n\n**Plan B**:\n- There is a 1/3 probability that all 900 jobs will be saved.\n- There is a 2/3 probability that 450 of them will be saved.\n\n**Analysis**:\n- Plan A guarantees 300 jobs saved.\n- Plan B has a higher probability of saving all 900 jobs (2/3 probability).\n\n**Decision**:\nGiven the higher probability of saving all 900 jobs in Plan B, it is more likely to save the maximum number of jobs. Therefore, **DECISION: B** is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 83, "outcome": "unparseable", "prompt_sha256": "b860798b39d8d9d7b3c62b818127346fd97fdab93864c7b0e2265bc4aaaec381", "reason": "no_decision_line", "record_type": "trial", "rep": 6, "response_text": "I choose Plan B because it has a higher probability of saving all the forest. The probability of saving all 1200 hectares is 2/3, which is greater than the 1/2 probability of saving 400 hectares in Plan A. Therefore, Plan B is more likely to save the entire forest. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 84, "outcome": "valid", "prompt_sha256": "62e70abd9ad24812b47da8f7592c7ab8cb6d390ccf8c3d93bbc80870a3d5dc42", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 85, "outcome": "valid", "prompt_sha256": "7ce37b109a6117554cafe49f766780cfddce7aecf48ae4e189f014067b8fd91a", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To make a decision, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- This means 1/3 * 1200 = 400 hectares are saved.\n- There is a 2/3 probability that none of the 1200 hectares will be saved.\n- This means 2/3 * 1200 = 800 hectares are at risk.\n\n**Expected outcome for Plan A:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\n**Expected outcome for Plan B:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\nBoth plans result in 800 hectares at risk. However, Plan A is more certain (100% certainty that 400 hectares are saved), while Plan B has a 1/3 chance of saving all the forest and a 2/3 chance of saving none.\n\nGiven the certainty of Plan A, it is likely to be the preferred choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 86, "outcome": "valid", "prompt_sha256": "af723fc0d14a404f25cdec493fbf6352ff7a6c72b8708b0efe80f512c62c8ac9", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 87, "outcome": "valid", "prompt_sha256": "609f6b15c5f091890109f1ed87acb262a66c9c066ef107a40ecb8ad1b2708e81", "reason": null, "record_type": "trial", "rep": 4, "response_text": "To determine which plan is better, we need to calculate the expected number of surviving bee colonies under each plan.\n\n**Plan A:**\n- 400 bee colonies will survive.\n- Expected number of surviving colonies = 400.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 bee colonies will survive.\n- There is a 2/3 probability that 600 of them will survive.\n- Expected number of surviving colonies = (1/3 * 1200) + (2/3 * 600) = 400 + 400 = 800.\n\nComparing the two plans:\n- Plan A: 400 surviving colonies.\n- Plan B: 800 surviving colonies.\n\nSince 800 is greater than 400, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 88, "outcome": "unparseable", "prompt_sha256": "04c15b15999a53d35fb4e65beeed26b1493898927f2f31ba89ad00ad3bda146a", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "I choose Plan B because it has a higher probability of recovering all 2400 customer accounts. The probability of recovering all accounts is 2/3, which is greater than the 1/2 probability of recovering 800 accounts in Plan A. Therefore, Plan B has a higher chance of success and is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 89, "outcome": "valid", "prompt_sha256": "1c563d16650b9a1861b8a918a5fc095fa985bae391c727472ded49e111956d6c", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 90, "outcome": "valid", "prompt_sha256": "c1b6e632a60a74bd7dd9a2de8f85c336fd0e99acb3ad8b9c095b71f2fb06e5c2", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 91, "outcome": "valid", "prompt_sha256": "1a8cc04152b268084d2d2d31450895e813ec316543b604932ba8a71e12d85120", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 92, "outcome": "valid", "prompt_sha256": "202a72fcce05b6df32805d3c650b65e25a970be250588a4703903a18e9a26854", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 93, "outcome": "valid", "prompt_sha256": "3ff43ce8a0d0b488166f6d5c9d24b5394f04826e121c2aee03f85f6a374ab86f", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 94, "outcome": "valid", "prompt_sha256": "9b9dd3efc0b56a2ca9323c6aaba0383f1fd12292a5b336ef8697568f9932822b", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 95, "outcome": "valid", "prompt_sha256": "0037a5d0a4c92f3aee2d28d516a4da0148dd3d079dfe7254d8ae2fc30cbb8d49", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 96, "outcome": "unparseable", "prompt_sha256": "b860798b39d8d9d7b3c62b818127346fd97fdab93864c7b0e2265bc4aaaec381", "reason": "no_decision_line", "record_type": "trial", "rep": 5, "response_text": "I choose Plan B because it has a higher probability of saving all the forest. The probability of saving all 1200 hectares is 2/3, which is greater than the 1/2 probability of saving 400 hectares in Plan A. Therefore, Plan B is more likely to save the entire forest. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 97, "outcome": "valid", "prompt_sha256": "50992ee411c24578a6c94c48f18d1f62e237371c3b907df3a7a8163b85f95164", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all 450 manuscripts, which is 2/3. This is better than Plan A, which only saves 150 manuscripts. Therefore, Plan B is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 98, "outcome": "valid", "prompt_sha256": "f779bb83f28de5af7bd94b1d13bd6ffa3609db7e4c12d8aac2f55443d4d40d05", "reason": null, "record_type": "trial", "rep": 5, "response_text": "To determine which plan is better, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 800 hectares of forest will burn.\n- This means 400 hectares of forest will not burn.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 hectares of forest will burn.\n- There is a 2/3 probability that all 1200 hectares of forest will burn.\n\nLet's calculate the expected area burned under each plan:\n\n**Plan A:**\n- 800 hectares will burn.\n- 400 hectares will not burn.\n- Total area burned = 800 hectares.\n\n**Plan B:**\n- Probability of no burn = 1/3\n- Probability of all burn = 2/3\n- Expected area burned = (1/3) * 1200 + (2/3) * 1200\n- Expected area burned = 400 + 800\n- Expected area burned = 1200 hectares.\n\nComparing the two plans:\n- Plan A: 800 hectares burned\n- Plan B: 1200 hectares burned\n\nSince 1200 hectares is more than 800 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 99, "outcome": "valid", "prompt_sha256": "82a919ffdd0850c8641a7e1e27cdf001c0b687f39af014f286b0dca1d1684579", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving all 600 people, which is 2/3. While Plan A saves 200 people, it is not guaranteed to save everyone, and the probability of saving all 600 people is only 1/3.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 100, "outcome": "unparseable", "prompt_sha256": "054c24d095fa58ccf7841f14cb51d8b319a8e37d49a6a806f5c63ff1f34df6e0", "reason": "no_decision_line", "record_type": "trial", "rep": 5, "response_text": "I choose Plan B because it has a higher probability of saving the grain. The probability that all 3000 tonnes of grain will be lost is 2/3, which is higher than the 1/3 probability that none of the grain will be lost. Therefore, Plan B has a higher chance of saving the grain. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 101, "outcome": "valid", "prompt_sha256": "3dac32627ae92008819ace85add0277dab846de18ba623aeb3d24b690ab398a2", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 102, "outcome": "unparseable", "prompt_sha256": "97d449dc071c07d4c0dbc5edfaa5003a341a490e5d490a47bdb1a7e6ef0d6f91", "reason": "no_decision_line", "record_type": "trial", "rep": 3, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which is 2/3 or approximately 66.67%. This means that there is a higher chance that all the businesses will be able to recover from the earthquake damage.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 103, "outcome": "valid", "prompt_sha256": "b1f4ebde4bb72094296c6a1945bef8947aecd7f37a7d5dfae89f3f2f664a190e", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 104, "outcome": "valid", "prompt_sha256": "82d2a53aac7c7424aa923dc583070f8c7d55d730469c3dd9fb9b51dac1e732b0", "reason": null, "record_type": "trial", "rep": 5, "response_text": "To make a decision, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 110 small businesses will survive.\n- This means 220 small businesses will not survive (330 - 110).\n\n**Plan B:**\n- There is a 1/3 probability that all 330 small businesses will survive.\n- If all 330 small businesses survive, 0 small businesses will not survive.\n- The probability that 165 small businesses will survive is 2/3.\n- If 165 small businesses survive, 165 small businesses will not survive.\n\n**Expected outcomes:**\n- For Plan A: 220 small businesses will not survive.\n- For Plan B: 165 small businesses will not survive.\n\n**Comparison:**\n- Plan A: 220 small businesses will not survive.\n- Plan B: 165 small businesses will not survive.\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 105, "outcome": "valid", "prompt_sha256": "2f12a26dd43f83eb1aeec2cbb75f6c45ab39b64f72c1ff2a52d562de4172cf3b", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 106, "outcome": "valid", "prompt_sha256": "2a61ba6fd082c73c52e19f8a8c6cfa5bd419b2b4a5ec7ee668789bbefad759d5", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 107, "outcome": "valid", "prompt_sha256": "0037a5d0a4c92f3aee2d28d516a4da0148dd3d079dfe7254d8ae2fc30cbb8d49", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 108, "outcome": "valid", "prompt_sha256": "3dac32627ae92008819ace85add0277dab846de18ba623aeb3d24b690ab398a2", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 109, "outcome": "valid", "prompt_sha256": "44af3fe999f4069a756676e5f18745d3dc53dd88bf5c50c4c235fe7d216f285e", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 110, "outcome": "valid", "prompt_sha256": "61de0674f34c6486b832f34789da59450032b069498e736fd4f98399eef14ec4", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 111, "outcome": "valid", "prompt_sha256": "f7919b5fc8130327438f795590feb847cd19c3a5a4fe54e4e20fda29d5fa445b", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 112, "outcome": "unparseable", "prompt_sha256": "04c15b15999a53d35fb4e65beeed26b1493898927f2f31ba89ad00ad3bda146a", "reason": "no_decision_line", "record_type": "trial", "rep": 4, "response_text": "I choose Plan B because it has a higher probability of recovering all 2400 customer accounts. The probability of recovering all accounts is 2/3, which is greater than the 1/2 probability of recovering 800 accounts in Plan A. Therefore, Plan B has a higher chance of success and is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 113, "outcome": "valid", "prompt_sha256": "2515d3452ef63d4c84da44d45d127c0b304a79651c8a63c9b037ecb151f7e661", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 114, "outcome": "valid", "prompt_sha256": "9b9dd3efc0b56a2ca9323c6aaba0383f1fd12292a5b336ef8697568f9932822b", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 115, "outcome": "valid", "prompt_sha256": "3a2812b1f6ff45945aba3a1f8fe040678be464b9257e87217477718df27aae89", "reason": null, "record_type": "trial", "rep": 2, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of all 60 wolves surviving is 2/3, which is greater than the 1/2 probability of 20 wolves surviving in Plan A.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 116, "outcome": "valid", "prompt_sha256": "6da92acdf593a53b086a34c5977d4efbb037b5cf15af9fde10624ec223abe322", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 117, "outcome": "valid", "prompt_sha256": "9e9e3283093d3563304522958f60061c120a343b538e2458c276ace96937356d", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To make an informed decision, let's evaluate both plans:\n\n**Plan A:**\n- 800 customer accounts will be recovered.\n- This plan guarantees that 800 accounts are recovered, but it does not provide any information about the recovery of the remaining 1600 accounts.\n\n**Plan B:**\n- There is a 1/3 probability that all 2400 customer accounts will be recovered.\n- There is a 2/3 probability that 1200 of them will be recovered.\n- This plan has a higher probability of recovering all 2400 accounts, which is 2/3.\n\nGiven the goal is to recover as many customer accounts as possible, **Plan B** is the better choice because it has a higher probability of recovering all 2400 accounts.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 118, "outcome": "unparseable", "prompt_sha256": "6c9079d4c77962ddbb0633c6b4cbd4a96d8847cf97116cfb2eba9f7347056a2c", "reason": "no_decision_line", "record_type": "trial", "rep": 5, "response_text": "To determine which plan to choose, we need to evaluate the expected number of people saved under each plan.\n\n**Plan A:**\n- Number of people saved: 200\n- Expected number of people saved: 200\n\n**Plan B:**\n- Probability that all 600 people are saved: 1/3\n- Probability that 300 people are saved: 2/3\n- Expected number of people saved: (1/3) * 600 + (2/3) * 300 = 200 + 200 = 400\n\nComparing the two plans:\n- Plan A saves 200 people.\n- Plan B saves 400 people.\n\nTherefore, **DECISION: B**", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 119, "outcome": "unparseable", "prompt_sha256": "283907a9d79322a05d7786e0caa70a2b10f75ea5f75f7066226e4df3601ff261", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which could be beneficial for the town's economy. However, I also consider the potential risk of all businesses failing, which could lead to significant economic losses. The decision should be based on a thorough analysis of the potential outcomes and the town's priorities. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A_prime"}
{"error": null, "index": 120, "outcome": "valid", "prompt_sha256": "7456b71c362a6775251f56cf83130c772ae0e691c54be63667cc6bb2bdeab731", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 121, "outcome": "valid", "prompt_sha256": "5a8846970ae24c83fc47585a39ed1f97828f5475f6e761190adef24e34259087", "reason": null, "record_type": "trial", "rep": 4, "response_text": "To determine which plan is better, we need to calculate the expected number of bee colonies that will die under each plan.\n\n**Plan A:**\n- 800 bee colonies will die.\n- Expected number of bee colonies that will die = 800.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 bee colonies will die.\n- There is a 2/3 probability that all 1200 bee colonies will die.\n- Expected number of bee colonies that will die = (1/3) * 0 + (2/3) * 1200 = 2400.\n\nComparing the two plans:\n- Plan A: 800 bee colonies will die.\n- Plan B: 2400 bee colonies will die.\n\nSince 2400 is greater than 800, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 122, "outcome": "unparseable", "prompt_sha256": "14569a8f52b1d4fdcf54b3cce7058f6302ed0351ba7ab4af83a221e778a2c69d", "reason": "no_decision_line", "record_type": "trial", "rep": 3, "response_text": "To determine which plan is better, we need to calculate the expected number of small businesses that will close under each plan.\n\n**Plan A:**\n- If 220 small businesses close, the expected number of small businesses that will close is 220.\n\n**Plan B:**\n- The probability that none of the 330 small businesses will close is \\( \\frac{1}{3} \\).\n- The probability that all 330 small businesses will close is \\( \\frac{2}{3} \\).\n- The expected number of small businesses that will close is the sum of the probabilities of each scenario:\n  \\[\n  \\text{Expected number of small businesses closing} = \\left(\\frac{1}{3} \\times 0\\right) + \\left(\\frac{2}{3} \\times 330\\right) = 0 + 220 = 220\n  \\]\n\nBoth plans result in the same expected number of small businesses closing, which is 220. Therefore, either plan A or plan B would be equally effective in terms of the expected number of small businesses that will close.\n\nDECISION: A or B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 123, "outcome": "valid", "prompt_sha256": "6da92acdf593a53b086a34c5977d4efbb037b5cf15af9fde10624ec223abe322", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B\n\nPlan B has a higher probability of ensuring that all 1800 children stay healthy, which is 2/3. This is a safer option given the risk of contamination.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 124, "outcome": "unparseable", "prompt_sha256": "04c15b15999a53d35fb4e65beeed26b1493898927f2f31ba89ad00ad3bda146a", "reason": "no_decision_line", "record_type": "trial", "rep": 2, "response_text": "I choose Plan B because it has a higher probability of recovering all 2400 customer accounts. The probability of recovering all accounts is 2/3, which is greater than the 1/2 probability of recovering 800 accounts in Plan A. Therefore, Plan B has a higher chance of success and is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 125, "outcome": "valid", "prompt_sha256": "3ff43ce8a0d0b488166f6d5c9d24b5394f04826e121c2aee03f85f6a374ab86f", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 126, "outcome": "valid", "prompt_sha256": "62e70abd9ad24812b47da8f7592c7ab8cb6d390ccf8c3d93bbc80870a3d5dc42", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 127, "outcome": "unparseable", "prompt_sha256": "971ed7f2dce2ec386643f1b74bb6490221ec7cfd67f97fd65d50746c5a2dbe1e", "reason": "no_decision_line", "record_type": "trial", "rep": 3, "response_text": "To make an informed decision, let's analyze the two plans:\n\n**Plan A** involves losing 600 jobs out of 900. This means 300 jobs will be saved.\n\n**Plan B** involves a 1/3 probability of losing none of the jobs and a 2/3 probability of losing all the jobs. This means the probability of saving 300 jobs is 2/3.\n\nComparing the probabilities:\n- Plan A: 300 jobs saved with a probability of 100%.\n- Plan B: 300 jobs saved with a probability of 2/3.\n\nSince the probability of saving 300 jobs is higher in Plan B, it is more likely that 300 jobs will be saved under Plan B.\n\nTherefore, DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 128, "outcome": "valid", "prompt_sha256": "7456b71c362a6775251f56cf83130c772ae0e691c54be63667cc6bb2bdeab731", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 129, "outcome": "valid", "prompt_sha256": "6f6a83bf4757e9c741a22d9062334779fa13e40146ed4d3295dd6a37baea92f9", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 130, "outcome": "valid", "prompt_sha256": "f7919b5fc8130327438f795590feb847cd19c3a5a4fe54e4e20fda29d5fa445b", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 131, "outcome": "valid", "prompt_sha256": "82a919ffdd0850c8641a7e1e27cdf001c0b687f39af014f286b0dca1d1684579", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving all 600 people, which is 2/3. While Plan A saves 200 people, it is not guaranteed to save everyone, and the probability of saving all 600 people is only 1/3.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 132, "outcome": "valid", "prompt_sha256": "2e86fbbfe9a7b06217fd1acf990117bbdce93a2b153d8090bec775dd36385fc4", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving the most homes, which is 2/3 of 1500 homes, equaling 1000 homes. This is better than Plan A, which saves only 500 homes.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 133, "outcome": "unparseable", "prompt_sha256": "14569a8f52b1d4fdcf54b3cce7058f6302ed0351ba7ab4af83a221e778a2c69d", "reason": "no_decision_line", "record_type": "trial", "rep": 4, "response_text": "To determine which plan is better, we need to calculate the expected number of small businesses that will close under each plan.\n\n**Plan A:**\n- If 220 small businesses close, the expected number of small businesses that will close is 220.\n\n**Plan B:**\n- The probability that none of the 330 small businesses will close is \\( \\frac{1}{3} \\).\n- The probability that all 330 small businesses will close is \\( \\frac{2}{3} \\).\n- The expected number of small businesses that will close is the sum of the probabilities of each scenario:\n  \\[\n  \\text{Expected number of small businesses closing} = \\left(\\frac{1}{3} \\times 0\\right) + \\left(\\frac{2}{3} \\times 330\\right) = 0 + 220 = 220\n  \\]\n\nBoth plans result in the same expected number of small businesses closing, which is 220. Therefore, either plan A or plan B would be equally effective in terms of the expected number of small businesses that will close.\n\nDECISION: A or B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 134, "outcome": "valid", "prompt_sha256": "48e4375c003683cb043d91bb17a4199165510c73f521c156070a8a36ac45b2f8", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 135, "outcome": "valid", "prompt_sha256": "6da92acdf593a53b086a34c5977d4efbb037b5cf15af9fde10624ec223abe322", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 136, "outcome": "unparseable", "prompt_sha256": "054c24d095fa58ccf7841f14cb51d8b319a8e37d49a6a806f5c63ff1f34df6e0", "reason": "no_decision_line", "record_type": "trial", "rep": 2, "response_text": "I choose Plan B because it has a higher probability of saving the grain. The probability that all 3000 tonnes of grain will be lost is 2/3, which is higher than the 1/3 probability that none of the grain will be lost. Therefore, Plan B has a higher chance of saving the grain. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 137, "outcome": "valid", "prompt_sha256": "c1b6e632a60a74bd7dd9a2de8f85c336fd0e99acb3ad8b9c095b71f2fb06e5c2", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 138, "outcome": "valid", "prompt_sha256": "61de0674f34c6486b832f34789da59450032b069498e736fd4f98399eef14ec4", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 139, "outcome": "valid", "prompt_sha256": "2f12a26dd43f83eb1aeec2cbb75f6c45ab39b64f72c1ff2a52d562de4172cf3b", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 140, "outcome": "unparseable", "prompt_sha256": "054c24d095fa58ccf7841f14cb51d8b319a8e37d49a6a806f5c63ff1f34df6e0", "reason": "no_decision_line", "record_type": "trial", "rep": 7, "response_text": "I choose Plan B because it has a higher probability of saving the grain. The probability that all 3000 tonnes of grain will be lost is 2/3, which is higher than the 1/3 probability that none of the grain will be lost. Therefore, Plan B has a higher chance of saving the grain. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 141, "outcome": "unparseable", "prompt_sha256": "6c9079d4c77962ddbb0633c6b4cbd4a96d8847cf97116cfb2eba9f7347056a2c", "reason": "no_decision_line", "record_type": "trial", "rep": 6, "response_text": "To determine which plan to choose, we need to evaluate the expected number of people saved under each plan.\n\n**Plan A:**\n- Number of people saved: 200\n- Expected number of people saved: 200\n\n**Plan B:**\n- Probability that all 600 people are saved: 1/3\n- Probability that 300 people are saved: 2/3\n- Expected number of people saved: (1/3) * 600 + (2/3) * 300 = 200 + 200 = 400\n\nComparing the two plans:\n- Plan A saves 200 people.\n- Plan B saves 400 people.\n\nTherefore, **DECISION: B**", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 142, "outcome": "valid", "prompt_sha256": "5a8846970ae24c83fc47585a39ed1f97828f5475f6e761190adef24e34259087", "reason": null, "record_type": "trial", "rep": 5, "response_text": "To determine which plan is better, we need to calculate the expected number of bee colonies that will die under each plan.\n\n**Plan A:**\n- 800 bee colonies will die.\n- Expected number of bee colonies that will die = 800.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 bee colonies will die.\n- There is a 2/3 probability that all 1200 bee colonies will die.\n- Expected number of bee colonies that will die = (1/3) * 0 + (2/3) * 1200 = 2400.\n\nComparing the two plans:\n- Plan A: 800 bee colonies will die.\n- Plan B: 2400 bee colonies will die.\n\nSince 2400 is greater than 800, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 143, "outcome": "valid", "prompt_sha256": "a1dfb76cbdbfc30ab5f7074bb5a16e5c206fcfae5f38e7ba2e0f7704c3ef8fc0", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 144, "outcome": "valid", "prompt_sha256": "edc8e86dfb51239157229f508ff6b6d21a8f64e12b98fcbbc34a85c05962d4a1", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 145, "outcome": "unparseable", "prompt_sha256": "08a12d85b996707087fe56ef0fb7217cd591c867e1943fe6754f073776f78515", "reason": "no_decision_line", "record_type": "trial", "rep": 5, "response_text": "I choose Plan B because it has a higher probability of saving the most people. With a 2/3 probability that all 600 people will die, it is more likely that fewer people will die compared to Plan A where 400 people will die. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 146, "outcome": "valid", "prompt_sha256": "3a482d3bb9cc14a151a39e85364b8086815ccc19dd82e2a0bccb9c1897e3d687", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the bee colonies, which is 2/3. Therefore, I would choose Plan B.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 147, "outcome": "valid", "prompt_sha256": "44af3fe999f4069a756676e5f18745d3dc53dd88bf5c50c4c235fe7d216f285e", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 148, "outcome": "valid", "prompt_sha256": "609f6b15c5f091890109f1ed87acb262a66c9c066ef107a40ecb8ad1b2708e81", "reason": null, "record_type": "trial", "rep": 5, "response_text": "To determine which plan is better, we need to calculate the expected number of surviving bee colonies under each plan.\n\n**Plan A:**\n- 400 bee colonies will survive.\n- Expected number of surviving colonies = 400.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 bee colonies will survive.\n- There is a 2/3 probability that 600 of them will survive.\n- Expected number of surviving colonies = (1/3 * 1200) + (2/3 * 600) = 400 + 400 = 800.\n\nComparing the two plans:\n- Plan A: 400 surviving colonies.\n- Plan B: 800 surviving colonies.\n\nSince 800 is greater than 400, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 149, "outcome": "valid", "prompt_sha256": "3a482d3bb9cc14a151a39e85364b8086815ccc19dd82e2a0bccb9c1897e3d687", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the bee colonies, which is 2/3. Therefore, I would choose Plan B.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 150, "outcome": "valid", "prompt_sha256": "d0671648f97b8cae3a3993c8036a30d1fed2b0ba872a5db8b7fb3d0691a0a88c", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 151, "outcome": "valid", "prompt_sha256": "edc8e86dfb51239157229f508ff6b6d21a8f64e12b98fcbbc34a85c05962d4a1", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 152, "outcome": "unparseable", "prompt_sha256": "82a919ffdd0850c8641a7e1e27cdf001c0b687f39af014f286b0dca1d1684579", "reason": "no_decision_line", "record_type": "trial", "rep": 6, "response_text": "I choose Plan B because it has a higher probability of saving all 600 people. The probability of saving all 600 people is 2/3, which is greater than the 1/2 probability of saving 200 people in Plan A. Therefore, Plan B is the better choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 153, "outcome": "unparseable", "prompt_sha256": "97d449dc071c07d4c0dbc5edfaa5003a341a490e5d490a47bdb1a7e6ef0d6f91", "reason": "no_decision_line", "record_type": "trial", "rep": 6, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which is 2/3 or approximately 66.67%. This means that there is a higher chance that all the businesses will be able to recover from the earthquake damage.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 154, "outcome": "valid", "prompt_sha256": "b1f4ebde4bb72094296c6a1945bef8947aecd7f37a7d5dfae89f3f2f664a190e", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 155, "outcome": "valid", "prompt_sha256": "2e86fbbfe9a7b06217fd1acf990117bbdce93a2b153d8090bec775dd36385fc4", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving the most homes, which is 2/3 of 1500 homes, equaling 1000 homes. This is better than Plan A, which saves only 500 homes.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 156, "outcome": "unparseable", "prompt_sha256": "971ed7f2dce2ec386643f1b74bb6490221ec7cfd67f97fd65d50746c5a2dbe1e", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "To make an informed decision, let's analyze the two plans:\n\n**Plan A** involves losing 600 jobs out of 900. This means 300 jobs will be saved.\n\n**Plan B** involves a 1/3 probability of losing none of the jobs and a 2/3 probability of losing all the jobs. This means the probability of saving 300 jobs is 2/3.\n\nComparing the probabilities:\n- Plan A: 300 jobs saved with a probability of 100%.\n- Plan B: 300 jobs saved with a probability of 2/3.\n\nSince the probability of saving 300 jobs is higher in Plan B, it is more likely that 300 jobs will be saved under Plan B.\n\nTherefore, DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 157, "outcome": "valid", "prompt_sha256": "1c92977460e44d1f46d69a7aafc171c8668d606223cdd8e43e02eece67d9c1f4", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 158, "outcome": "valid", "prompt_sha256": "48e4375c003683cb043d91bb17a4199165510c73f521c156070a8a36ac45b2f8", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 159, "outcome": "valid", "prompt_sha256": "af723fc0d14a404f25cdec493fbf6352ff7a6c72b8708b0efe80f512c62c8ac9", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 160, "outcome": "unparseable", "prompt_sha256": "b860798b39d8d9d7b3c62b818127346fd97fdab93864c7b0e2265bc4aaaec381", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "I choose Plan B because it has a higher probability of saving all the forest. The probability of saving all 1200 hectares is 2/3, which is greater than the 1/2 probability of saving 400 hectares in Plan A. Therefore, Plan B is more likely to save the entire forest. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 161, "outcome": "valid", "prompt_sha256": "3a2812b1f6ff45945aba3a1f8fe040678be464b9257e87217477718df27aae89", "reason": null, "record_type": "trial", "rep": 5, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of all 60 wolves surviving is 2/3, which is greater than the 1/2 probability of 20 wolves surviving in Plan A.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 162, "outcome": "valid", "prompt_sha256": "9e1c48da2e125cc47b13ec2d681224d329a8498e633e051f2b7f808a5794cbc9", "reason": null, "record_type": "trial", "rep": 7, "response_text": "To determine which plan is better, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk (1200 - 400).\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- There is a 2/3 probability that 600 hectares will be saved.\n\nLet's calculate the expected number of hectares saved in Plan B:\n\n- Probability of saving all 1200 hectares: \\( \\frac{1}{3} \\)\n- Probability of saving 600 hectares: \\( \\frac{2}{3} \\)\n\nExpected number of hectares saved = \\( \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\)\n\nNow, let's calculate this:\n\n\\[ \\text{Expected number of hectares saved} = \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\]\n\\[ = 400 + 400 \\]\n\\[ = 800 \\]\n\nSo, the expected number of hectares saved in Plan B is 800.\n\n**Comparison:**\n- Plan A saves 400 hectares.\n- Plan B saves 800 hectares.\n\nSince 800 hectares is more than 400 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 163, "outcome": "valid", "prompt_sha256": "2f12a26dd43f83eb1aeec2cbb75f6c45ab39b64f72c1ff2a52d562de4172cf3b", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 164, "outcome": "valid", "prompt_sha256": "61de0674f34c6486b832f34789da59450032b069498e736fd4f98399eef14ec4", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 165, "outcome": "valid", "prompt_sha256": "1a8cc04152b268084d2d2d31450895e813ec316543b604932ba8a71e12d85120", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 166, "outcome": "valid", "prompt_sha256": "babf36e242dfeca98d3a1bed8ca1669e13844b57270674d6ae43074f7efd64bc", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 167, "outcome": "valid", "prompt_sha256": "3ff43ce8a0d0b488166f6d5c9d24b5394f04826e121c2aee03f85f6a374ab86f", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 168, "outcome": "valid", "prompt_sha256": "1c92977460e44d1f46d69a7aafc171c8668d606223cdd8e43e02eece67d9c1f4", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 169, "outcome": "valid", "prompt_sha256": "82d2a53aac7c7424aa923dc583070f8c7d55d730469c3dd9fb9b51dac1e732b0", "reason": null, "record_type": "trial", "rep": 7, "response_text": "To make a decision, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 110 small businesses will survive.\n- This means 220 small businesses will not survive (330 - 110).\n\n**Plan B:**\n- There is a 1/3 probability that all 330 small businesses will survive.\n- If all 330 small businesses survive, 0 small businesses will not survive.\n- The probability that 165 small businesses will survive is 2/3.\n- If 165 small businesses survive, 165 small businesses will not survive.\n\n**Expected outcomes:**\n- For Plan A: 220 small businesses will not survive.\n- For Plan B: 165 small businesses will not survive.\n\n**Comparison:**\n- Plan A: 220 small businesses will not survive.\n- Plan B: 165 small businesses will not survive.\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 170, "outcome": "valid", "prompt_sha256": "0254fe148f9d0ebce727a3cf45400f5dcf858e9fa4dc7b7508c7c2f69c2e18b9", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B\n\nPlan B has a higher probability of ensuring that no children fall ill, which is 2/3. This is a safer option given the risk of contamination.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 171, "outcome": "valid", "prompt_sha256": "44af3fe999f4069a756676e5f18745d3dc53dd88bf5c50c4c235fe7d216f285e", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 172, "outcome": "valid", "prompt_sha256": "82a919ffdd0850c8641a7e1e27cdf001c0b687f39af014f286b0dca1d1684579", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving all 600 people, which is 2/3. While Plan A saves 200 people, it is not guaranteed to save everyone, and the probability of saving all 600 people is only 1/3.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 173, "outcome": "valid", "prompt_sha256": "48e4375c003683cb043d91bb17a4199165510c73f521c156070a8a36ac45b2f8", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 174, "outcome": "valid", "prompt_sha256": "7456b71c362a6775251f56cf83130c772ae0e691c54be63667cc6bb2bdeab731", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 175, "outcome": "valid", "prompt_sha256": "f061b5fa3edd89e2dd4fdeb8d9fd317564f8710838dc7bf7da7f41532b4b552e", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving all 900 jobs, which is 2/3. This means there is a 66.67% chance that all 900 jobs will be saved, compared to 30% chance with Plan A.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 176, "outcome": "valid", "prompt_sha256": "9b9dd3efc0b56a2ca9323c6aaba0383f1fd12292a5b336ef8697568f9932822b", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 177, "outcome": "valid", "prompt_sha256": "65cf4426caa5639fb56501a173e81174c3fc807466503a072ddb294e0d56cea0", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. Therefore, it is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 178, "outcome": "valid", "prompt_sha256": "a1dfb76cbdbfc30ab5f7074bb5a16e5c206fcfae5f38e7ba2e0f7704c3ef8fc0", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 179, "outcome": "valid", "prompt_sha256": "a1dfb76cbdbfc30ab5f7074bb5a16e5c206fcfae5f38e7ba2e0f7704c3ef8fc0", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 180, "outcome": "valid", "prompt_sha256": "9b9dd3efc0b56a2ca9323c6aaba0383f1fd12292a5b336ef8697568f9932822b", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 181, "outcome": "valid", "prompt_sha256": "babf36e242dfeca98d3a1bed8ca1669e13844b57270674d6ae43074f7efd64bc", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 182, "outcome": "valid", "prompt_sha256": "82d2a53aac7c7424aa923dc583070f8c7d55d730469c3dd9fb9b51dac1e732b0", "reason": null, "record_type": "trial", "rep": 2, "response_text": "To make a decision, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 110 small businesses will survive.\n- This means 220 small businesses will not survive (330 - 110).\n\n**Plan B:**\n- There is a 1/3 probability that all 330 small businesses will survive.\n- If all 330 small businesses survive, 0 small businesses will not survive.\n- The probability that 165 small businesses will survive is 2/3.\n- If 165 small businesses survive, 165 small businesses will not survive.\n\n**Expected outcomes:**\n- For Plan A: 220 small businesses will not survive.\n- For Plan B: 165 small businesses will not survive.\n\n**Comparison:**\n- Plan A: 220 small businesses will not survive.\n- Plan B: 165 small businesses will not survive.\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 183, "outcome": "valid", "prompt_sha256": "f7919b5fc8130327438f795590feb847cd19c3a5a4fe54e4e20fda29d5fa445b", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 184, "outcome": "valid", "prompt_sha256": "b1f4ebde4bb72094296c6a1945bef8947aecd7f37a7d5dfae89f3f2f664a190e", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 185, "outcome": "unparseable", "prompt_sha256": "97d449dc071c07d4c0dbc5edfaa5003a341a490e5d490a47bdb1a7e6ef0d6f91", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which is 2/3 or approximately 66.67%. This means that there is a higher chance that all the businesses will be able to recover from the earthquake damage.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 186, "outcome": "valid", "prompt_sha256": "2dffb1f6a6f42ac6e5d87674b0c1ff9267e851757704187f43f69e107ac2fb7b", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. This means there is a 1/3 chance that none of the grain will be saved, which is a risk that Plan A does not have. Therefore, Plan B is the safer choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 187, "outcome": "unparseable", "prompt_sha256": "97d449dc071c07d4c0dbc5edfaa5003a341a490e5d490a47bdb1a7e6ef0d6f91", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which is 2/3 or approximately 66.67%. This means that there is a higher chance that all the businesses will be able to recover from the earthquake damage.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 188, "outcome": "valid", "prompt_sha256": "3a482d3bb9cc14a151a39e85364b8086815ccc19dd82e2a0bccb9c1897e3d687", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 189, "outcome": "valid", "prompt_sha256": "65cf4426caa5639fb56501a173e81174c3fc807466503a072ddb294e0d56cea0", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. Therefore, it is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 190, "outcome": "valid", "prompt_sha256": "af723fc0d14a404f25cdec493fbf6352ff7a6c72b8708b0efe80f512c62c8ac9", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 191, "outcome": "valid", "prompt_sha256": "3ff43ce8a0d0b488166f6d5c9d24b5394f04826e121c2aee03f85f6a374ab86f", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 192, "outcome": "valid", "prompt_sha256": "1c563d16650b9a1861b8a918a5fc095fa985bae391c727472ded49e111956d6c", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 193, "outcome": "valid", "prompt_sha256": "3a482d3bb9cc14a151a39e85364b8086815ccc19dd82e2a0bccb9c1897e3d687", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the bee colonies, which is 2/3. Therefore, I would choose Plan B.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 194, "outcome": "unparseable", "prompt_sha256": "283907a9d79322a05d7786e0caa70a2b10f75ea5f75f7066226e4df3601ff261", "reason": "no_decision_line", "record_type": "trial", "rep": 5, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which could be beneficial for the town's economy. However, I also consider the potential risk of all businesses failing, which could lead to significant economic losses. The decision should be based on a thorough analysis of the potential outcomes and the town's priorities. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A_prime"}
{"error": null, "index": 195, "outcome": "valid", "prompt_sha256": "9e9e3283093d3563304522958f60061c120a343b538e2458c276ace96937356d", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To decide which plan to choose, we need to evaluate the expected number of customer accounts that will be recovered under each plan.\n\n**Plan A:**\n- 800 customer accounts will be recovered.\n- The probability of this happening is 100% (or 1).\n\n**Plan B:**\n- There is a 1/3 probability that all 2400 customer accounts will be recovered.\n- There is a 2/3 probability that 1200 of them will be recovered.\n\nLet's calculate the expected number of customer accounts recovered under each plan:\n\n**Plan A:**\n- Expected number of recovered accounts = 800\n\n**Plan B:**\n- Expected number of recovered accounts = (1/3) * 2400 + (2/3) * 1200\n- Expected number of recovered accounts = (1/3) * 2400 + (2/3) * 1200\n- Expected number of recovered accounts = 800 + 800\n- Expected number of recovered accounts = 1600\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 196, "outcome": "valid", "prompt_sha256": "f7919b5fc8130327438f795590feb847cd19c3a5a4fe54e4e20fda29d5fa445b", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 197, "outcome": "valid", "prompt_sha256": "2dffb1f6a6f42ac6e5d87674b0c1ff9267e851757704187f43f69e107ac2fb7b", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. This means there is a 1/3 chance that none of the grain will be saved, which is a risk that Plan A does not have. Therefore, Plan B is the safer option.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 198, "outcome": "valid", "prompt_sha256": "50992ee411c24578a6c94c48f18d1f62e237371c3b907df3a7a8163b85f95164", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all 450 manuscripts, which is 2/3. This is better than Plan A, which only saves 150 manuscripts. Therefore, Plan B is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 199, "outcome": "valid", "prompt_sha256": "3dac32627ae92008819ace85add0277dab846de18ba623aeb3d24b690ab398a2", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 200, "outcome": "valid", "prompt_sha256": "1da1ca378b35a1935d70110b76cb1a815ed98c1201a4114170324085cf772f2c", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nPlan B is chosen because it has a higher probability of saving the most homes, with a 2/3 probability that all 1500 homes will be saved.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 201, "outcome": "valid", "prompt_sha256": "9e1c48da2e125cc47b13ec2d681224d329a8498e633e051f2b7f808a5794cbc9", "reason": null, "record_type": "trial", "rep": 5, "response_text": "To determine which plan is better, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk (1200 - 400).\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- There is a 2/3 probability that 600 hectares will be saved.\n\nLet's calculate the expected number of hectares saved in Plan B:\n\n- Probability of saving all 1200 hectares: \\( \\frac{1}{3} \\)\n- Probability of saving 600 hectares: \\( \\frac{2}{3} \\)\n\nExpected number of hectares saved = \\( \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\)\n\nNow, let's calculate this:\n\n\\[ \\text{Expected number of hectares saved} = \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\]\n\\[ = 400 + 400 \\]\n\\[ = 800 \\]\n\nSo, the expected number of hectares saved in Plan B is 800.\n\n**Comparison:**\n- Plan A saves 400 hectares.\n- Plan B saves 800 hectares.\n\nSince 800 hectares is more than 400 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 202, "outcome": "unparseable", "prompt_sha256": "5a98c9b275f2e269f441136545c26e9517f770e42f2ac6673cafb36d6b5b3dee", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of saving all 60 wolves is 2/3, which is greater than the 1/2 probability of saving 20 wolves in Plan A. Therefore, Plan B is the better choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 203, "outcome": "valid", "prompt_sha256": "6da92acdf593a53b086a34c5977d4efbb037b5cf15af9fde10624ec223abe322", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 204, "outcome": "valid", "prompt_sha256": "7ce37b109a6117554cafe49f766780cfddce7aecf48ae4e189f014067b8fd91a", "reason": null, "record_type": "trial", "rep": 4, "response_text": "To make a decision, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- This means 1/3 * 1200 = 400 hectares are saved.\n- There is a 2/3 probability that none of the 1200 hectares will be saved.\n- This means 2/3 * 1200 = 800 hectares are at risk.\n\n**Expected outcome for Plan A:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\n**Expected outcome for Plan B:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\nBoth plans result in 800 hectares at risk. However, Plan A is more certain (100% certainty that 400 hectares are saved), while Plan B has a 1/3 chance of saving all the forest and a 2/3 chance of saving none.\n\nGiven the certainty of Plan A, it is likely to be the preferred choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 205, "outcome": "valid", "prompt_sha256": "6f6a83bf4757e9c741a22d9062334779fa13e40146ed4d3295dd6a37baea92f9", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 206, "outcome": "unparseable", "prompt_sha256": "08a12d85b996707087fe56ef0fb7217cd591c867e1943fe6754f073776f78515", "reason": "no_decision_line", "record_type": "trial", "rep": 2, "response_text": "I choose Plan B because it has a higher probability of saving the most people. With a 2/3 probability that all 600 people will die, it is more likely that fewer people will die compared to Plan A where 400 people will die. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 207, "outcome": "valid", "prompt_sha256": "65cf4426caa5639fb56501a173e81174c3fc807466503a072ddb294e0d56cea0", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. Therefore, it is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 208, "outcome": "valid", "prompt_sha256": "7ce37b109a6117554cafe49f766780cfddce7aecf48ae4e189f014067b8fd91a", "reason": null, "record_type": "trial", "rep": 3, "response_text": "To make a decision, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- This means 1/3 * 1200 = 400 hectares are saved.\n- There is a 2/3 probability that none of the 1200 hectares will be saved.\n- This means 2/3 * 1200 = 800 hectares are at risk.\n\n**Expected outcome for Plan A:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\n**Expected outcome for Plan B:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\nBoth plans result in 800 hectares at risk. However, Plan A is more certain (100% certainty that 400 hectares are saved), while Plan B has a 1/3 chance of saving all the forest and a 2/3 chance of saving none.\n\nGiven the certainty of Plan A, it is likely to be the preferred choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 209, "outcome": "valid", "prompt_sha256": "7ce37b109a6117554cafe49f766780cfddce7aecf48ae4e189f014067b8fd91a", "reason": null, "record_type": "trial", "rep": 7, "response_text": "To make an informed decision, let's analyze both plans:\n\n- **Plan A** saves 400 hectares of forest. This means that 800 hectares are at risk.\n- **Plan B** has a 1/3 probability of saving all 1200 hectares and a 2/3 probability of saving none. This means that 800 hectares are at risk.\n\nBoth plans leave 800 hectares at risk. However, Plan B has a higher probability of saving the entire forest, which could be seen as a more reliable solution.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 210, "outcome": "valid", "prompt_sha256": "b860798b39d8d9d7b3c62b818127346fd97fdab93864c7b0e2265bc4aaaec381", "reason": null, "record_type": "trial", "rep": 4, "response_text": "To make a decision, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- This means 1/3 * 1200 = 400 hectares are saved.\n- There is a 2/3 probability that none of the 1200 hectares will be saved.\n- This means 2/3 * 1200 = 800 hectares are at risk.\n\n**Expected outcomes:**\n- Plan A: 800 hectares at risk.\n- Plan B: 800 hectares at risk.\n\nBoth plans result in 800 hectares at risk. Since both plans are equally likely to result in the same outcome, either plan can be chosen. However, if we consider the probability of each plan being chosen, Plan B has a higher probability of being chosen (2/3).\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 211, "outcome": "valid", "prompt_sha256": "5a8846970ae24c83fc47585a39ed1f97828f5475f6e761190adef24e34259087", "reason": null, "record_type": "trial", "rep": 7, "response_text": "To determine which plan is better, we need to calculate the expected number of bee colonies that will die under each plan.\n\n**Plan A:**\n- 800 bee colonies will die.\n- Expected number of bee colonies that will die = 800.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 bee colonies will die.\n- There is a 2/3 probability that all 1200 bee colonies will die.\n- Expected number of bee colonies that will die = (1/3) * 0 + (2/3) * 1200 = 2400.\n\nComparing the two plans:\n- Plan A: 800 bee colonies will die.\n- Plan B: 2400 bee colonies will die.\n\nSince 2400 is greater than 800, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 212, "outcome": "unparseable", "prompt_sha256": "b860798b39d8d9d7b3c62b818127346fd97fdab93864c7b0e2265bc4aaaec381", "reason": "no_decision_line", "record_type": "trial", "rep": 7, "response_text": "I choose Plan B because it has a higher probability of saving all the forest. The probability of saving all 1200 hectares is 2/3, which is greater than the 1/2 probability of saving 400 hectares in Plan A. Therefore, Plan B is more likely to save the entire forest. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 213, "outcome": "valid", "prompt_sha256": "6da92acdf593a53b086a34c5977d4efbb037b5cf15af9fde10624ec223abe322", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 214, "outcome": "valid", "prompt_sha256": "f061b5fa3edd89e2dd4fdeb8d9fd317564f8710838dc7bf7da7f41532b4b552e", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving all 900 jobs, which is 2/3. This means there is a 66.67% chance that all 900 jobs will be saved, compared to 30% chance with Plan A.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 215, "outcome": "valid", "prompt_sha256": "202a72fcce05b6df32805d3c650b65e25a970be250588a4703903a18e9a26854", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 216, "outcome": "valid", "prompt_sha256": "4d8e93c9e42a796c0ac1cc83878390f80c0fe9783ffdbe06acc522e3d8a8ecbc", "reason": null, "record_type": "trial", "rep": 7, "response_text": "To make an informed decision, let's evaluate both plans:\n\n**Plan A:**\n- 1000 tonnes of grain are saved.\n- This plan saves 1000 tonnes out of 3000 tonnes at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 3000 tonnes will be saved.\n- There is a 2/3 probability that 1500 tonnes will be saved.\n\nLet's calculate the expected amount of grain saved under each plan:\n\n**Plan A:**\n- Expected grain saved = 1000 tonnes\n\n**Plan B:**\n- Expected grain saved = (1/3) * 3000 tonnes + (2/3) * 1500 tonnes\n- Expected grain saved = 1000 tonnes + 1000 tonnes\n- Expected grain saved = 2000 tonnes\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 217, "outcome": "valid", "prompt_sha256": "202a72fcce05b6df32805d3c650b65e25a970be250588a4703903a18e9a26854", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 218, "outcome": "valid", "prompt_sha256": "2f12a26dd43f83eb1aeec2cbb75f6c45ab39b64f72c1ff2a52d562de4172cf3b", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 219, "outcome": "valid", "prompt_sha256": "3a2812b1f6ff45945aba3a1f8fe040678be464b9257e87217477718df27aae89", "reason": null, "record_type": "trial", "rep": 4, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of all 60 wolves surviving is 2/3, which is greater than the 1/2 probability of 20 wolves surviving in Plan A.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 220, "outcome": "unparseable", "prompt_sha256": "6c9079d4c77962ddbb0633c6b4cbd4a96d8847cf97116cfb2eba9f7347056a2c", "reason": "no_decision_line", "record_type": "trial", "rep": 7, "response_text": "To determine which plan to choose, we need to evaluate the expected number of people saved under each plan.\n\n**Plan A:**\n- Number of people saved: 200\n- Expected number of people saved: 200\n\n**Plan B:**\n- Probability that all 600 people are saved: 1/3\n- Probability that 300 people are saved: 2/3\n- Expected number of people saved: (1/3) * 600 + (2/3) * 300 = 200 + 200 = 400\n\nComparing the two plans:\n- Plan A saves 200 people.\n- Plan B saves 400 people.\n\nTherefore, **DECISION: B**", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 221, "outcome": "unparseable", "prompt_sha256": "edc8e86dfb51239157229f508ff6b6d21a8f64e12b98fcbbc34a85c05962d4a1", "reason": "no_decision_line", "record_type": "trial", "rep": 7, "response_text": "I choose Plan B because it has a higher probability of saving all 600 people. The probability of saving all 600 people is 2/3, which is greater than the 200 people saved by Plan A. Therefore, Plan B is the better choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "A_prime"}
{"error": null, "index": 222, "outcome": "unparseable", "prompt_sha256": "5c8abb5e6b883ad75d1f9dc04712057abca573f1acb486179e174bdfd2272ccf", "reason": "no_decision_line", "record_type": "trial", "rep": 2, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts saved under each plan.\n\n**Plan A:**\n- 150 manuscripts are saved.\n- Expected number of manuscripts saved = 150 manuscripts.\n\n**Plan B:**\n- There is a 1/3 probability that all 450 manuscripts will be saved.\n- There is a 2/3 probability that 225 of them will be saved.\n- Expected number of manuscripts saved = (1/3 * 450) + (2/3 * 225) = 150 + 150 = 300 manuscripts.\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 300 manuscripts.\n\nTherefore, **DECISION: B** is the better plan.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 223, "outcome": "valid", "prompt_sha256": "82d2a53aac7c7424aa923dc583070f8c7d55d730469c3dd9fb9b51dac1e732b0", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To make a decision, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 110 small businesses will survive.\n- This means 220 small businesses will not survive (330 - 110).\n\n**Plan B:**\n- There is a 1/3 probability that all 330 small businesses will survive.\n- If all 330 small businesses survive, 0 small businesses will not survive.\n- The probability that 165 small businesses will survive is 2/3.\n- If 165 small businesses survive, 165 small businesses will not survive.\n\n**Expected outcomes:**\n- For Plan A: 220 small businesses will not survive.\n- For Plan B: 165 small businesses will not survive.\n\n**Comparison:**\n- Plan A: 220 small businesses will not survive.\n- Plan B: 165 small businesses will not survive.\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 224, "outcome": "unparseable", "prompt_sha256": "edc8e86dfb51239157229f508ff6b6d21a8f64e12b98fcbbc34a85c05962d4a1", "reason": "no_decision_line", "record_type": "trial", "rep": 5, "response_text": "I choose Plan B because it has a higher probability of saving all 600 people. The probability of saving all 600 people is 2/3, which is greater than the 200 people saved by Plan A. Therefore, Plan B is the better choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "A_prime"}
{"error": null, "index": 225, "outcome": "valid", "prompt_sha256": "0037a5d0a4c92f3aee2d28d516a4da0148dd3d079dfe7254d8ae2fc30cbb8d49", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 226, "outcome": "valid", "prompt_sha256": "6b679de71f8b792fd776a5f20b33d5942ada39e2bd68023b782f7336f5574be3", "reason": null, "record_type": "trial", "rep": 4, "response_text": "To determine which plan to choose, we need to calculate the expected number of containers that will be recovered under each plan.\n\n**Plan A:**\n- Number of containers recovered: 150\n- Expected number of containers recovered: 150\n\n**Plan B:**\n- Probability that all 450 containers are recovered: 1/3\n- Probability that 225 containers are recovered: 2/3\n- Expected number of containers recovered: (1/3) * 450 + (2/3) * 225 = 150 + 150 = 300\n\nComparing the expected values:\n- Plan A: 150 containers\n- Plan B: 300 containers\n\nSince 300 containers is greater than 150 containers, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 227, "outcome": "valid", "prompt_sha256": "babf36e242dfeca98d3a1bed8ca1669e13844b57270674d6ae43074f7efd64bc", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 228, "outcome": "valid", "prompt_sha256": "a1dfb76cbdbfc30ab5f7074bb5a16e5c206fcfae5f38e7ba2e0f7704c3ef8fc0", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 229, "outcome": "valid", "prompt_sha256": "c1b6e632a60a74bd7dd9a2de8f85c336fd0e99acb3ad8b9c095b71f2fb06e5c2", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 230, "outcome": "valid", "prompt_sha256": "202a72fcce05b6df32805d3c650b65e25a970be250588a4703903a18e9a26854", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 231, "outcome": "valid", "prompt_sha256": "b2b47b9f50a439165a0bca2338d37d0fb0d4c74a7cb5641a188b0e2e121c2fa8", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B\n\nPlan B has a higher probability of not losing any containers, which is 2/3. This means there is a 1/3 chance that all 450 containers will be lost, which is a higher risk than Plan A where 300 containers are lost. Therefore, Plan B is the safer option.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 232, "outcome": "unparseable", "prompt_sha256": "6c9079d4c77962ddbb0633c6b4cbd4a96d8847cf97116cfb2eba9f7347056a2c", "reason": "no_decision_line", "record_type": "trial", "rep": 3, "response_text": "To determine which plan to choose, we need to evaluate the expected number of people saved under each plan.\n\n**Plan A:**\n- Number of people saved: 200\n- Expected number of people saved: 200\n\n**Plan B:**\n- Probability that all 600 people are saved: 1/3\n- Probability that 300 people are saved: 2/3\n- Expected number of people saved: (1/3) * 600 + (2/3) * 300 = 200 + 200 = 400\n\nComparing the two plans:\n- Plan A saves 200 people.\n- Plan B saves 400 people.\n\nTherefore, **DECISION: B**", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 233, "outcome": "unparseable", "prompt_sha256": "5c8abb5e6b883ad75d1f9dc04712057abca573f1acb486179e174bdfd2272ccf", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts saved under each plan.\n\n**Plan A:**\n- 150 manuscripts are saved.\n- Expected number of manuscripts saved = 150 manuscripts.\n\n**Plan B:**\n- There is a 1/3 probability that all 450 manuscripts will be saved.\n- There is a 2/3 probability that 225 of them will be saved.\n- Expected number of manuscripts saved = (1/3 * 450) + (2/3 * 225) = 150 + 150 = 300 manuscripts.\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 300 manuscripts.\n\nTherefore, **DECISION: B** is the better plan.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 234, "outcome": "valid", "prompt_sha256": "65cf4426caa5639fb56501a173e81174c3fc807466503a072ddb294e0d56cea0", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. Therefore, it is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 235, "outcome": "valid", "prompt_sha256": "50992ee411c24578a6c94c48f18d1f62e237371c3b907df3a7a8163b85f95164", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all 450 manuscripts, which is 2/3. This is better than Plan A, which only saves 150 manuscripts. Therefore, Plan B is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 236, "outcome": "unparseable", "prompt_sha256": "5e1719a222df3cef8fbbbaec3016aad6b8eb78fbccec220eaadb49462b17f8b2", "reason": "no_decision_line", "record_type": "trial", "rep": 5, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability that all 60 wolves will die is 2/3, which is higher than the 1/2 probability that 40 wolves will die in Plan A. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 237, "outcome": "valid", "prompt_sha256": "9e1c48da2e125cc47b13ec2d681224d329a8498e633e051f2b7f808a5794cbc9", "reason": null, "record_type": "trial", "rep": 3, "response_text": "To determine which plan is better, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk (1200 - 400).\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- There is a 2/3 probability that 600 hectares will be saved.\n\nLet's calculate the expected number of hectares saved in Plan B:\n\n- Probability of saving all 1200 hectares: \\( \\frac{1}{3} \\)\n- Probability of saving 600 hectares: \\( \\frac{2}{3} \\)\n\nExpected number of hectares saved = \\( \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\)\n\nNow, let's calculate this:\n\n\\[ \\text{Expected number of hectares saved} = \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\]\n\\[ = 400 + 400 \\]\n\\[ = 800 \\]\n\nSo, the expected number of hectares saved in Plan B is 800.\n\n**Comparison:**\n- Plan A saves 400 hectares.\n- Plan B saves 800 hectares.\n\nSince 800 hectares is more than 400 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 238, "outcome": "valid", "prompt_sha256": "48e4375c003683cb043d91bb17a4199165510c73f521c156070a8a36ac45b2f8", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 239, "outcome": "valid", "prompt_sha256": "971ed7f2dce2ec386643f1b74bb6490221ec7cfd67f97fd65d50746c5a2dbe1e", "reason": null, "record_type": "trial", "rep": 7, "response_text": "To make an informed decision, let's analyze the two plans:\n\n**Plan A:** 600 jobs will be lost.\n**Plan B:** There is a 1/3 probability that none of the 900 jobs will be lost, and a 2/3 probability that all 900 jobs will be lost.\n\n**Analysis:**\n- Plan A results in the loss of 600 jobs.\n- Plan B results in the loss of either 0 or 900 jobs, with probabilities of 1/3 and 2/3 respectively.\n\n**Decision:**\nGiven the potential outcomes, Plan B has a higher probability of not losing any jobs (2/3 chance), which is more favorable than losing 600 jobs (Plan A).\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 240, "outcome": "valid", "prompt_sha256": "d0671648f97b8cae3a3993c8036a30d1fed2b0ba872a5db8b7fb3d0691a0a88c", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 241, "outcome": "valid", "prompt_sha256": "2515d3452ef63d4c84da44d45d127c0b304a79651c8a63c9b037ecb151f7e661", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 242, "outcome": "valid", "prompt_sha256": "af723fc0d14a404f25cdec493fbf6352ff7a6c72b8708b0efe80f512c62c8ac9", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 243, "outcome": "valid", "prompt_sha256": "9e9e3283093d3563304522958f60061c120a343b538e2458c276ace96937356d", "reason": null, "record_type": "trial", "rep": 5, "response_text": "To make an informed decision, let's evaluate both plans:\n\n**Plan A:**\n- 800 customer accounts will be recovered.\n- This plan guarantees that 800 accounts are recovered, but it does not provide any information about the recovery of the remaining 1600 accounts.\n\n**Plan B:**\n- There is a 1/3 probability that all 2400 customer accounts will be recovered.\n- There is a 2/3 probability that 1200 of them will be recovered.\n- This plan has a higher probability of recovering all 2400 accounts, which is 2/3.\n\nGiven the goal is to recover as many customer accounts as possible, **Plan B** is the better choice because it has a higher probability of recovering all 2400 accounts.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 244, "outcome": "unparseable", "prompt_sha256": "6c9079d4c77962ddbb0633c6b4cbd4a96d8847cf97116cfb2eba9f7347056a2c", "reason": "no_decision_line", "record_type": "trial", "rep": 4, "response_text": "To determine which plan to choose, we need to evaluate the expected number of people saved under each plan.\n\n**Plan A:**\n- Number of people saved: 200\n- Expected number of people saved: 200\n\n**Plan B:**\n- Probability that all 600 people are saved: 1/3\n- Probability that 300 people are saved: 2/3\n- Expected number of people saved: (1/3) * 600 + (2/3) * 300 = 200 + 200 = 400\n\nComparing the two plans:\n- Plan A saves 200 people.\n- Plan B saves 400 people.\n\nTherefore, **DECISION: B**", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 245, "outcome": "valid", "prompt_sha256": "3a2812b1f6ff45945aba3a1f8fe040678be464b9257e87217477718df27aae89", "reason": null, "record_type": "trial", "rep": 6, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of all 60 wolves surviving is 2/3, which is greater than the 1/2 probability of 20 wolves surviving in Plan A.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 246, "outcome": "valid", "prompt_sha256": "609f6b15c5f091890109f1ed87acb262a66c9c066ef107a40ecb8ad1b2708e81", "reason": null, "record_type": "trial", "rep": 6, "response_text": "To determine which plan is better, we need to calculate the expected number of surviving bee colonies under each plan.\n\n**Plan A:**\n- 400 bee colonies will survive.\n- Expected number of surviving colonies = 400.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 bee colonies will survive.\n- There is a 2/3 probability that 600 of them will survive.\n- Expected number of surviving colonies = (1/3 * 1200) + (2/3 * 600) = 400 + 400 = 800.\n\nComparing the two plans:\n- Plan A: 400 surviving colonies.\n- Plan B: 800 surviving colonies.\n\nSince 800 is greater than 400, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 247, "outcome": "unparseable", "prompt_sha256": "6c9079d4c77962ddbb0633c6b4cbd4a96d8847cf97116cfb2eba9f7347056a2c", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "To determine which plan to choose, we need to evaluate the expected number of people saved under each plan.\n\n**Plan A:**\n- Number of people saved: 200\n- Expected number of people saved: 200\n\n**Plan B:**\n- Probability that all 600 people are saved: 1/3\n- Probability that 300 people are saved: 2/3\n- Expected number of people saved: (1/3) * 600 + (2/3) * 300 = 200 + 200 = 400\n\nComparing the two plans:\n- Plan A saves 200 people.\n- Plan B saves 400 people.\n\nTherefore, **DECISION: B**", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 248, "outcome": "valid", "prompt_sha256": "f061b5fa3edd89e2dd4fdeb8d9fd317564f8710838dc7bf7da7f41532b4b552e", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving all 900 jobs, which is 2/3. This means there is a 66.67% chance that all 900 jobs will be saved, compared to 30% chance with Plan A.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 249, "outcome": "unparseable", "prompt_sha256": "054c24d095fa58ccf7841f14cb51d8b319a8e37d49a6a806f5c63ff1f34df6e0", "reason": "no_decision_line", "record_type": "trial", "rep": 3, "response_text": "I choose Plan B because it has a higher probability of saving the grain. The probability that all 3000 tonnes of grain will be lost is 2/3, which is higher than the 1/3 probability that none of the grain will be lost. Therefore, Plan B has a higher chance of saving the grain. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 250, "outcome": "valid", "prompt_sha256": "3a2812b1f6ff45945aba3a1f8fe040678be464b9257e87217477718df27aae89", "reason": null, "record_type": "trial", "rep": 1, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of all 60 wolves surviving is 2/3, which is greater than the 1/2 probability of 20 wolves surviving in Plan A.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 251, "outcome": "valid", "prompt_sha256": "b558642e1cbc215a0d6ee27711872ff8da30bbaff5fb318a952158b4ee6e44df", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To make an informed decision, let's analyze the probabilities and potential outcomes for each plan:\n\n**Plan A:**\n- 1600 customer accounts will be lost.\n- This means 800 accounts will be saved.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 2400 customer accounts will be lost.\n- If this happens, 2400 accounts will be saved.\n- There is a 2/3 probability that all 2400 customer accounts will be lost.\n- If this happens, 0 accounts will be saved.\n\n**Analysis:**\n- Plan A guarantees 800 accounts will be saved.\n- Plan B has a 2/3 chance of saving all 2400 accounts, which is the best outcome.\n\n**Conclusion:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 252, "outcome": "unparseable", "prompt_sha256": "08a12d85b996707087fe56ef0fb7217cd591c867e1943fe6754f073776f78515", "reason": "no_decision_line", "record_type": "trial", "rep": 3, "response_text": "I choose Plan B because it has a higher probability of saving the most people. With a 2/3 probability that all 600 people will die, it is more likely that fewer people will die compared to Plan A where 400 people will die. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 253, "outcome": "valid", "prompt_sha256": "5a8846970ae24c83fc47585a39ed1f97828f5475f6e761190adef24e34259087", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To determine which plan is better, we need to calculate the expected number of bee colonies that will die under each plan.\n\n**Plan A:**\n- 800 bee colonies will die.\n- Expected number of bee colonies that will die = 800.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 bee colonies will die.\n- There is a 2/3 probability that all 1200 bee colonies will die.\n- Expected number of bee colonies that will die = (1/3) * 0 + (2/3) * 1200 = 2400.\n\nComparing the two plans:\n- Plan A: 800 bee colonies will die.\n- Plan B: 2400 bee colonies will die.\n\nSince 2400 is greater than 800, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 254, "outcome": "valid", "prompt_sha256": "6b679de71f8b792fd776a5f20b33d5942ada39e2bd68023b782f7336f5574be3", "reason": null, "record_type": "trial", "rep": 5, "response_text": "To determine which plan to choose, we need to calculate the expected number of containers that will be recovered under each plan.\n\n**Plan A:**\n- Number of containers recovered: 150\n- Expected number of containers recovered: 150\n\n**Plan B:**\n- Probability that all 450 containers are recovered: 1/3\n- Probability that 225 containers are recovered: 2/3\n- Expected number of containers recovered: (1/3) * 450 + (2/3) * 225 = 150 + 150 = 300\n\nComparing the expected values:\n- Plan A: 150 containers\n- Plan B: 300 containers\n\nSince 300 containers is greater than 150 containers, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 255, "outcome": "valid", "prompt_sha256": "1a8cc04152b268084d2d2d31450895e813ec316543b604932ba8a71e12d85120", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 256, "outcome": "unparseable", "prompt_sha256": "5a98c9b275f2e269f441136545c26e9517f770e42f2ac6673cafb36d6b5b3dee", "reason": "no_decision_line", "record_type": "trial", "rep": 2, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of saving all 60 wolves is 2/3, which is greater than the 1/2 probability of saving 20 wolves in Plan A. Therefore, Plan B is the better choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 257, "outcome": "valid", "prompt_sha256": "44af3fe999f4069a756676e5f18745d3dc53dd88bf5c50c4c235fe7d216f285e", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 258, "outcome": "valid", "prompt_sha256": "6f6a83bf4757e9c741a22d9062334779fa13e40146ed4d3295dd6a37baea92f9", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 259, "outcome": "unparseable", "prompt_sha256": "6c9079d4c77962ddbb0633c6b4cbd4a96d8847cf97116cfb2eba9f7347056a2c", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "To determine which plan to choose, we need to evaluate the expected number of people saved under each plan.\n\n**Plan A:**\n- Number of people saved: 200\n- Expected number of people saved: 200\n\n**Plan B:**\n- Probability that all 600 people are saved: 1/3\n- Probability that 300 people are saved: 2/3\n- Expected number of people saved: (1/3) * 600 + (2/3) * 300 = 200 + 200 = 400\n\nComparing the two plans:\n- Plan A saves 200 people.\n- Plan B saves 400 people.\n\nTherefore, **DECISION: B**", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 260, "outcome": "valid", "prompt_sha256": "b2b47b9f50a439165a0bca2338d37d0fb0d4c74a7cb5641a188b0e2e121c2fa8", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B\n\nPlan B has a higher probability of not losing any containers, which is 2/3. This means there is a 1/3 chance that all 450 containers will be lost, which is a higher risk than Plan A where 300 containers are lost. Therefore, Plan B is the safer option.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 261, "outcome": "valid", "prompt_sha256": "3ff43ce8a0d0b488166f6d5c9d24b5394f04826e121c2aee03f85f6a374ab86f", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 262, "outcome": "valid", "prompt_sha256": "787d760c34f474b373009fcd12fecdd425575c516b9d03a7cbb3593eeaa56eb7", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts that will be saved under each plan.\n\n**Plan A:**\n- Number of manuscripts destroyed: 300\n- Number of manuscripts saved: 450 - 300 = 150\n\n**Plan B:**\n- Probability that none of the manuscripts are destroyed: 1/3\n- Probability that all 450 manuscripts are destroyed: 2/3\n\nThe expected number of manuscripts saved under Plan B can be calculated as follows:\n\\[ \\text{Expected number of manuscripts saved} = \\left(\\frac{1}{3} \\times 150\\right) + \\left(\\frac{2}{3} \\times 0\\right) = \\frac{150}{3} = 50 \\]\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 50 manuscripts.\n\nTherefore, Plan A is the better choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 263, "outcome": "valid", "prompt_sha256": "2515d3452ef63d4c84da44d45d127c0b304a79651c8a63c9b037ecb151f7e661", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 264, "outcome": "valid", "prompt_sha256": "2e86fbbfe9a7b06217fd1acf990117bbdce93a2b153d8090bec775dd36385fc4", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving the most homes, which is 2/3 of 1500 homes, equaling 1000 homes. This is better than Plan A, which saves only 500 homes.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 265, "outcome": "valid", "prompt_sha256": "f779bb83f28de5af7bd94b1d13bd6ffa3609db7e4c12d8aac2f55443d4d40d05", "reason": null, "record_type": "trial", "rep": 6, "response_text": "To determine which plan is better, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 800 hectares of forest will burn.\n- This means 400 hectares of forest will not burn.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 hectares of forest will burn.\n- There is a 2/3 probability that all 1200 hectares of forest will burn.\n\nLet's calculate the expected area burned under each plan:\n\n**Plan A:**\n- 800 hectares will burn.\n- 400 hectares will not burn.\n- Total area burned = 800 hectares.\n\n**Plan B:**\n- Probability of no burn = 1/3\n- Probability of all burn = 2/3\n- Expected area burned = (1/3) * 1200 + (2/3) * 1200\n- Expected area burned = 400 + 800\n- Expected area burned = 1200 hectares.\n\nComparing the two plans:\n- Plan A: 800 hectares burned\n- Plan B: 1200 hectares burned\n\nSince 1200 hectares is more than 800 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 266, "outcome": "valid", "prompt_sha256": "787d760c34f474b373009fcd12fecdd425575c516b9d03a7cbb3593eeaa56eb7", "reason": null, "record_type": "trial", "rep": 5, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts that will be saved under each plan.\n\n**Plan A:**\n- Number of manuscripts destroyed: 300\n- Number of manuscripts saved: 450 - 300 = 150\n\n**Plan B:**\n- Probability that none of the manuscripts are destroyed: 1/3\n- Probability that all 450 manuscripts are destroyed: 2/3\n\nThe expected number of manuscripts saved under Plan B can be calculated as follows:\n\\[ \\text{Expected number of manuscripts saved} = \\left(\\frac{1}{3} \\times 150\\right) + \\left(\\frac{2}{3} \\times 0\\right) = \\frac{150}{3} = 50 \\]\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 50 manuscripts.\n\nTherefore, Plan A is the better choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 267, "outcome": "valid", "prompt_sha256": "6f6a83bf4757e9c741a22d9062334779fa13e40146ed4d3295dd6a37baea92f9", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 268, "outcome": "valid", "prompt_sha256": "609f6b15c5f091890109f1ed87acb262a66c9c066ef107a40ecb8ad1b2708e81", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To determine which plan is better, we need to calculate the expected number of surviving bee colonies under each plan.\n\n**Plan A:**\n- 400 bee colonies will survive.\n- Expected number of surviving colonies = 400.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 bee colonies will survive.\n- There is a 2/3 probability that 600 of them will survive.\n- Expected number of surviving colonies = (1/3 * 1200) + (2/3 * 600) = 400 + 400 = 800.\n\nComparing the two plans:\n- Plan A: 400 surviving colonies.\n- Plan B: 800 surviving colonies.\n\nSince 800 is greater than 400, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 269, "outcome": "valid", "prompt_sha256": "44af3fe999f4069a756676e5f18745d3dc53dd88bf5c50c4c235fe7d216f285e", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 270, "outcome": "valid", "prompt_sha256": "04c15b15999a53d35fb4e65beeed26b1493898927f2f31ba89ad00ad3bda146a", "reason": null, "record_type": "trial", "rep": 6, "response_text": "I choose Plan B because it has a higher probability of recovering all 2400 customer accounts. The probability of recovering all accounts is 2/3, which is greater than the 1/2 probability of recovering 800 accounts in Plan A.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 271, "outcome": "valid", "prompt_sha256": "787d760c34f474b373009fcd12fecdd425575c516b9d03a7cbb3593eeaa56eb7", "reason": null, "record_type": "trial", "rep": 6, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts that will be saved under each plan.\n\n**Plan A:**\n- Number of manuscripts destroyed: 300\n- Number of manuscripts saved: 450 - 300 = 150\n\n**Plan B:**\n- Probability that none of the manuscripts are destroyed: 1/3\n- Probability that all 450 manuscripts are destroyed: 2/3\n\nThe expected number of manuscripts saved under Plan B can be calculated as follows:\n\\[ \\text{Expected number of manuscripts saved} = \\left(\\frac{1}{3} \\times 150\\right) + \\left(\\frac{2}{3} \\times 0\\right) = \\frac{150}{3} = 50 \\]\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 50 manuscripts.\n\nTherefore, Plan A is the better choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 272, "outcome": "valid", "prompt_sha256": "5c8abb5e6b883ad75d1f9dc04712057abca573f1acb486179e174bdfd2272ccf", "reason": null, "record_type": "trial", "rep": 7, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts saved under each plan.\n\n**Plan A:**\n- Number of manuscripts saved: 150\n\n**Plan B:**\n- Probability that all 450 manuscripts are saved: \\( \\frac{1}{3} \\)\n- Probability that 225 manuscripts are saved: \\( \\frac{2}{3} \\)\n\nExpected number of manuscripts saved under Plan B:\n\\[ \\text{Expected number} = \\left( \\frac{1}{3} \\times 450 \\right) + \\left( \\frac{2}{3} \\times 225 \\right) \\]\n\nLet's calculate this:\n\n\\[ \\text{Expected number} = \\left( \\frac{1}{3} \\times 450 \\right) + \\left( \\frac{2}{3} \\times 225 \\right) \\]\n\\[ \\text{Expected number} = 150 + 150 \\]\n\\[ \\text{Expected number} = 300 \\]\n\n**Comparison:**\n- Plan A: 150 manuscripts saved\n- Plan B: 300 manuscripts saved\n\nSince 300 manuscripts saved is greater than 150 manuscripts saved, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 273, "outcome": "valid", "prompt_sha256": "6b679de71f8b792fd776a5f20b33d5942ada39e2bd68023b782f7336f5574be3", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To determine which plan to choose, we need to calculate the expected number of containers that will be recovered under each plan.\n\n**Plan A:**\n- Number of containers recovered: 150\n- Expected number of containers recovered: 150\n\n**Plan B:**\n- Probability that all 450 containers are recovered: 1/3\n- Probability that 225 containers are recovered: 2/3\n- Expected number of containers recovered: (1/3) * 450 + (2/3) * 225 = 150 + 150 = 300\n\nComparing the expected values:\n- Plan A: 150 containers\n- Plan B: 300 containers\n\nSince 300 containers is greater than 150 containers, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 274, "outcome": "valid", "prompt_sha256": "2515d3452ef63d4c84da44d45d127c0b304a79651c8a63c9b037ecb151f7e661", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 275, "outcome": "valid", "prompt_sha256": "202a72fcce05b6df32805d3c650b65e25a970be250588a4703903a18e9a26854", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 276, "outcome": "valid", "prompt_sha256": "82d2a53aac7c7424aa923dc583070f8c7d55d730469c3dd9fb9b51dac1e732b0", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To make a decision, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 110 small businesses will survive.\n- This means 220 small businesses will not survive (330 - 110).\n\n**Plan B:**\n- There is a 1/3 probability that all 330 small businesses will survive.\n- If all 330 small businesses survive, 0 small businesses will not survive.\n- The probability that 165 small businesses will survive is 2/3.\n- If 165 small businesses survive, 165 small businesses will not survive.\n\n**Expected outcomes:**\n- For Plan A: 220 small businesses will not survive.\n- For Plan B: 165 small businesses will not survive.\n\n**Comparison:**\n- Plan A: 220 small businesses will not survive.\n- Plan B: 165 small businesses will not survive.\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 277, "outcome": "valid", "prompt_sha256": "283907a9d79322a05d7786e0caa70a2b10f75ea5f75f7066226e4df3601ff261", "reason": null, "record_type": "trial", "rep": 6, "response_text": "To make a decision, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 110 small businesses will survive.\n- The probability of this happening is 110/330 = 1/3.\n\n**Plan B:**\n- There is a 1/3 probability that all 330 small businesses will survive.\n- There is a 2/3 probability that none of them will survive.\n\n**Expected Outcome of Plan A:**\n- Expected number of surviving businesses = 330 * (1/3) = 110.\n\n**Expected Outcome of Plan B:**\n- Expected number of surviving businesses = 330 * (1/3) + 330 * (2/3) = 110 + 220 = 330.\n\nSince Plan A results in 110 surviving businesses, which is less than the 330 expected to survive under Plan B, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 278, "outcome": "unparseable", "prompt_sha256": "283907a9d79322a05d7786e0caa70a2b10f75ea5f75f7066226e4df3601ff261", "reason": "no_decision_line", "record_type": "trial", "rep": 7, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which could be beneficial for the town's economy. However, I also consider the potential risk of all businesses failing, which could lead to significant economic losses. Ultimately, I would need more information to make a final decision. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A_prime"}
{"error": null, "index": 279, "outcome": "valid", "prompt_sha256": "2a61ba6fd082c73c52e19f8a8c6cfa5bd419b2b4a5ec7ee668789bbefad759d5", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 280, "outcome": "valid", "prompt_sha256": "2a61ba6fd082c73c52e19f8a8c6cfa5bd419b2b4a5ec7ee668789bbefad759d5", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 281, "outcome": "valid", "prompt_sha256": "1a8cc04152b268084d2d2d31450895e813ec316543b604932ba8a71e12d85120", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 282, "outcome": "unparseable", "prompt_sha256": "054c24d095fa58ccf7841f14cb51d8b319a8e37d49a6a806f5c63ff1f34df6e0", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "I choose Plan B because it has a higher probability of saving the grain. The probability that all 3000 tonnes of grain will be lost is 2/3, which is higher than the 1/3 probability that none of the grain will be lost. Therefore, Plan B has a higher chance of saving the grain. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 283, "outcome": "valid", "prompt_sha256": "d0671648f97b8cae3a3993c8036a30d1fed2b0ba872a5db8b7fb3d0691a0a88c", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 284, "outcome": "valid", "prompt_sha256": "0037a5d0a4c92f3aee2d28d516a4da0148dd3d079dfe7254d8ae2fc30cbb8d49", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 285, "outcome": "unparseable", "prompt_sha256": "5a98c9b275f2e269f441136545c26e9517f770e42f2ac6673cafb36d6b5b3dee", "reason": "no_decision_line", "record_type": "trial", "rep": 3, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of saving all 60 wolves is 2/3, which is greater than the 1/2 probability of saving 20 wolves in Plan A. Therefore, Plan B is the better choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 286, "outcome": "valid", "prompt_sha256": "9e9e3283093d3563304522958f60061c120a343b538e2458c276ace96937356d", "reason": null, "record_type": "trial", "rep": 7, "response_text": "To make an informed decision, let's evaluate both plans:\n\n**Plan A:**\n- 800 customer accounts will be recovered.\n- This plan guarantees that 800 accounts are recovered, but it does not provide any information about the recovery of the remaining 1600 accounts.\n\n**Plan B:**\n- There is a 1/3 probability that all 2400 customer accounts will be recovered.\n- There is a 2/3 probability that 1200 of them will be recovered.\n- This plan has a higher probability of recovering all 2400 accounts, which is 2/3.\n\nGiven the goal is to recover as many customer accounts as possible, **Plan B** is the better choice because it has a higher probability of recovering all 2400 accounts.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 287, "outcome": "valid", "prompt_sha256": "0037a5d0a4c92f3aee2d28d516a4da0148dd3d079dfe7254d8ae2fc30cbb8d49", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 288, "outcome": "valid", "prompt_sha256": "f7919b5fc8130327438f795590feb847cd19c3a5a4fe54e4e20fda29d5fa445b", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 289, "outcome": "valid", "prompt_sha256": "d0671648f97b8cae3a3993c8036a30d1fed2b0ba872a5db8b7fb3d0691a0a88c", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 290, "outcome": "unparseable", "prompt_sha256": "5c8abb5e6b883ad75d1f9dc04712057abca573f1acb486179e174bdfd2272ccf", "reason": "no_decision_line", "record_type": "trial", "rep": 6, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts saved under each plan.\n\n**Plan A:**\n- 150 manuscripts are saved.\n- Expected number of manuscripts saved = 150 manuscripts.\n\n**Plan B:**\n- There is a 1/3 probability that all 450 manuscripts will be saved.\n- There is a 2/3 probability that 225 of them will be saved.\n- Expected number of manuscripts saved = (1/3 * 450) + (2/3 * 225) = 150 + 150 = 300 manuscripts.\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 300 manuscripts.\n\nTherefore, **DECISION: B** is the better plan.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 291, "outcome": "valid", "prompt_sha256": "6da92acdf593a53b086a34c5977d4efbb037b5cf15af9fde10624ec223abe322", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 292, "outcome": "unparseable", "prompt_sha256": "04c15b15999a53d35fb4e65beeed26b1493898927f2f31ba89ad00ad3bda146a", "reason": "no_decision_line", "record_type": "trial", "rep": 7, "response_text": "I choose Plan B because it has a higher probability of recovering all 2400 customer accounts. The probability of recovering all accounts is 2/3, which is greater than the 1/2 probability of recovering 800 accounts in Plan A. Therefore, Plan B has a higher chance of success and is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 293, "outcome": "valid", "prompt_sha256": "7ce37b109a6117554cafe49f766780cfddce7aecf48ae4e189f014067b8fd91a", "reason": null, "record_type": "trial", "rep": 6, "response_text": "To make a decision, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- This means 1/3 * 1200 = 400 hectares are saved.\n- There is a 2/3 probability that none of the 1200 hectares will be saved.\n- This means 2/3 * 1200 = 800 hectares are at risk.\n\n**Expected outcome for Plan A:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\n**Expected outcome for Plan B:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\nBoth plans result in 800 hectares at risk. However, Plan A is more certain (100% certainty that 400 hectares are saved), while Plan B has a 1/3 chance of saving all the forest and a 2/3 chance of saving none.\n\nGiven the certainty of Plan A, it is likely to be the preferred choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 294, "outcome": "valid", "prompt_sha256": "6b679de71f8b792fd776a5f20b33d5942ada39e2bd68023b782f7336f5574be3", "reason": null, "record_type": "trial", "rep": 6, "response_text": "To determine which plan to choose, we need to calculate the expected number of containers that will be recovered under each plan.\n\n**Plan A:**\n- Number of containers recovered: 150\n- Expected number of containers recovered: 150\n\n**Plan B:**\n- Probability that all 450 containers are recovered: 1/3\n- Probability that 225 containers are recovered: 2/3\n- Expected number of containers recovered: (1/3) * 450 + (2/3) * 225 = 150 + 150 = 300\n\nComparing the expected values:\n- Plan A: 150 containers\n- Plan B: 300 containers\n\nSince 300 containers is greater than 150 containers, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 295, "outcome": "unparseable", "prompt_sha256": "08a12d85b996707087fe56ef0fb7217cd591c867e1943fe6754f073776f78515", "reason": "no_decision_line", "record_type": "trial", "rep": 4, "response_text": "I choose Plan B because it has a higher probability of saving the most people. With a 2/3 probability that all 600 people will die, it is more likely that fewer people will die compared to Plan A where 400 people will die. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 296, "outcome": "valid", "prompt_sha256": "61de0674f34c6486b832f34789da59450032b069498e736fd4f98399eef14ec4", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 297, "outcome": "valid", "prompt_sha256": "a1dfb76cbdbfc30ab5f7074bb5a16e5c206fcfae5f38e7ba2e0f7704c3ef8fc0", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 298, "outcome": "unparseable", "prompt_sha256": "5c8abb5e6b883ad75d1f9dc04712057abca573f1acb486179e174bdfd2272ccf", "reason": "no_decision_line", "record_type": "trial", "rep": 4, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts saved under each plan.\n\n**Plan A:**\n- 150 manuscripts are saved.\n- Expected number of manuscripts saved = 150 manuscripts.\n\n**Plan B:**\n- There is a 1/3 probability that all 450 manuscripts will be saved.\n- There is a 2/3 probability that 225 of them will be saved.\n- Expected number of manuscripts saved = (1/3 * 450) + (2/3 * 225) = 150 + 150 = 300 manuscripts.\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 300 manuscripts.\n\nTherefore, **DECISION: B** is the better plan.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 299, "outcome": "valid", "prompt_sha256": "0254fe148f9d0ebce727a3cf45400f5dcf858e9fa4dc7b7508c7c2f69c2e18b9", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nPlan B has a higher probability of ensuring that no children fall ill, which is 2/3. This is a safer option given the risk of contamination.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 300, "outcome": "valid", "prompt_sha256": "44af3fe999f4069a756676e5f18745d3dc53dd88bf5c50c4c235fe7d216f285e", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 301, "outcome": "unparseable", "prompt_sha256": "283907a9d79322a05d7786e0caa70a2b10f75ea5f75f7066226e4df3601ff261", "reason": "no_decision_line", "record_type": "trial", "rep": 3, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which could be beneficial for the town's economy. However, I also consider the potential risk of all businesses failing, which could lead to significant economic losses. The decision should be based on a thorough analysis of the potential outcomes and the town's priorities. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A_prime"}
{"error": null, "index": 302, "outcome": "valid", "prompt_sha256": "1da1ca378b35a1935d70110b76cb1a815ed98c1201a4114170324085cf772f2c", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 303, "outcome": "unparseable", "prompt_sha256": "5a98c9b275f2e269f441136545c26e9517f770e42f2ac6673cafb36d6b5b3dee", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of saving all 60 wolves is 2/3, which is greater than the 1/2 probability of saving 20 wolves in Plan A. Therefore, Plan B is the better choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 304, "outcome": "valid", "prompt_sha256": "82a919ffdd0850c8641a7e1e27cdf001c0b687f39af014f286b0dca1d1684579", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving all 600 people, which is 2/3. While Plan A saves 200 people, it is not guaranteed to save everyone, and the probability of saving all 600 people is only 1/3.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 305, "outcome": "valid", "prompt_sha256": "1c563d16650b9a1861b8a918a5fc095fa985bae391c727472ded49e111956d6c", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 306, "outcome": "valid", "prompt_sha256": "787d760c34f474b373009fcd12fecdd425575c516b9d03a7cbb3593eeaa56eb7", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts that will be saved under each plan.\n\n**Plan A:**\n- Number of manuscripts destroyed: 300\n- Number of manuscripts saved: 450 - 300 = 150\n\n**Plan B:**\n- Probability that none of the manuscripts are destroyed: 1/3\n- Probability that all 450 manuscripts are destroyed: 2/3\n\nThe expected number of manuscripts saved under Plan B can be calculated as follows:\n\\[ \\text{Expected number of manuscripts saved} = \\left(\\frac{1}{3} \\times 150\\right) + \\left(\\frac{2}{3} \\times 0\\right) = \\frac{150}{3} = 50 \\]\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 50 manuscripts.\n\nTherefore, Plan A is the better choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 307, "outcome": "valid", "prompt_sha256": "5a8846970ae24c83fc47585a39ed1f97828f5475f6e761190adef24e34259087", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To determine which plan is better, we need to calculate the expected number of bee colonies that will die under each plan.\n\n**Plan A:**\n- 800 bee colonies will die.\n- Expected number of bee colonies that will die = 800.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 bee colonies will die.\n- There is a 2/3 probability that all 1200 bee colonies will die.\n- Expected number of bee colonies that will die = (1/3) * 0 + (2/3) * 1200 = 2400.\n\nComparing the two plans:\n- Plan A: 800 bee colonies will die.\n- Plan B: 2400 bee colonies will die.\n\nSince 2400 is greater than 800, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 308, "outcome": "valid", "prompt_sha256": "0254fe148f9d0ebce727a3cf45400f5dcf858e9fa4dc7b7508c7c2f69c2e18b9", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B\n\nPlan B has a higher probability of ensuring that no children fall ill, which is 2/3. This is a safer option given the risk of contamination.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 309, "outcome": "valid", "prompt_sha256": "7ce37b109a6117554cafe49f766780cfddce7aecf48ae4e189f014067b8fd91a", "reason": null, "record_type": "trial", "rep": 2, "response_text": "To make a decision, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- This means 1/3 * 1200 = 400 hectares are saved.\n- There is a 2/3 probability that none of the 1200 hectares will be saved.\n- This means 2/3 * 1200 = 800 hectares are at risk.\n\n**Expected outcome for Plan A:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\n**Expected outcome for Plan B:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\nBoth plans result in 800 hectares at risk. However, Plan A is more certain (100% certainty that 400 hectares are saved), while Plan B has a 1/3 chance of saving all the forest and a 2/3 chance of saving none.\n\nGiven the certainty of Plan A, it is likely to be the preferred choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 310, "outcome": "unparseable", "prompt_sha256": "6c9079d4c77962ddbb0633c6b4cbd4a96d8847cf97116cfb2eba9f7347056a2c", "reason": "no_decision_line", "record_type": "trial", "rep": 2, "response_text": "To determine which plan to choose, we need to evaluate the expected number of people saved under each plan.\n\n**Plan A:**\n- Number of people saved: 200\n- Expected number of people saved: 200\n\n**Plan B:**\n- Probability that all 600 people are saved: 1/3\n- Probability that 300 people are saved: 2/3\n- Expected number of people saved: (1/3) * 600 + (2/3) * 300 = 200 + 200 = 400\n\nComparing the two plans:\n- Plan A saves 200 people.\n- Plan B saves 400 people.\n\nTherefore, **DECISION: B**", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 311, "outcome": "unparseable", "prompt_sha256": "054c24d095fa58ccf7841f14cb51d8b319a8e37d49a6a806f5c63ff1f34df6e0", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of saving the grain. The probability that all 3000 tonnes of grain will be lost is 2/3, which is higher than the 1/3 probability that none of the grain will be lost. Therefore, Plan B has a higher chance of saving the grain. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 312, "outcome": "unparseable", "prompt_sha256": "5c8abb5e6b883ad75d1f9dc04712057abca573f1acb486179e174bdfd2272ccf", "reason": "no_decision_line", "record_type": "trial", "rep": 5, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts saved under each plan.\n\n**Plan A:**\n- 150 manuscripts are saved.\n- Expected number of manuscripts saved = 150 manuscripts.\n\n**Plan B:**\n- There is a 1/3 probability that all 450 manuscripts will be saved.\n- There is a 2/3 probability that 225 of them will be saved.\n- Expected number of manuscripts saved = (1/3 * 450) + (2/3 * 225) = 150 + 150 = 300 manuscripts.\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 300 manuscripts.\n\nTherefore, **DECISION: B** is the better plan.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 313, "outcome": "valid", "prompt_sha256": "1da1ca378b35a1935d70110b76cb1a815ed98c1201a4114170324085cf772f2c", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B\n\nPlan B is chosen because it has a higher probability of saving the most homes, with a 2/3 probability that all 1500 homes will be saved.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 314, "outcome": "valid", "prompt_sha256": "b558642e1cbc215a0d6ee27711872ff8da30bbaff5fb318a952158b4ee6e44df", "reason": null, "record_type": "trial", "rep": 4, "response_text": "To make an informed decision, let's analyze the probabilities and potential outcomes for each plan:\n\n**Plan A:**\n- 1600 customer accounts will be lost.\n- This means 800 accounts will be saved.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 2400 customer accounts will be lost.\n- If this happens, 2400 accounts will be saved.\n- There is a 2/3 probability that all 2400 customer accounts will be lost.\n- If this happens, 0 accounts will be saved.\n\n**Analysis:**\n- Plan A guarantees 800 accounts will be saved.\n- Plan B has a 2/3 chance of saving all 2400 accounts, which is the best outcome.\n\n**Conclusion:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 315, "outcome": "valid", "prompt_sha256": "b558642e1cbc215a0d6ee27711872ff8da30bbaff5fb318a952158b4ee6e44df", "reason": null, "record_type": "trial", "rep": 6, "response_text": "To make an informed decision, let's analyze both plans:\n\n**Plan A**:\n- 1600 customer accounts will be lost.\n\n**Plan B**:\n- There is a 1/3 probability that none of the 2400 customer accounts will be lost.\n- There is a 2/3 probability that all 2400 customer accounts will be lost.\n\n**Analysis**:\n- Plan A is straightforward and guarantees that 1600 customer accounts will be lost.\n- Plan B involves a probabilistic outcome, where the probability of losing all accounts is 2/3, which is higher than the guaranteed loss in Plan A.\n\n**Conclusion**:\nGiven the probabilistic nature of Plan B, where the risk of losing all accounts is higher, Plan B is riskier and potentially more costly in terms of lost data.\n\n**DECISION: B**", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 316, "outcome": "valid", "prompt_sha256": "d0671648f97b8cae3a3993c8036a30d1fed2b0ba872a5db8b7fb3d0691a0a88c", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 317, "outcome": "valid", "prompt_sha256": "2e86fbbfe9a7b06217fd1acf990117bbdce93a2b153d8090bec775dd36385fc4", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving the most homes, which is 2/3 of 1500 homes, equaling 1000 homes. This is better than Plan A, which saves only 500 homes.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 318, "outcome": "valid", "prompt_sha256": "7456b71c362a6775251f56cf83130c772ae0e691c54be63667cc6bb2bdeab731", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 319, "outcome": "unparseable", "prompt_sha256": "08a12d85b996707087fe56ef0fb7217cd591c867e1943fe6754f073776f78515", "reason": "no_decision_line", "record_type": "trial", "rep": 7, "response_text": "I choose Plan B because it has a higher probability of saving the most people. With a 2/3 probability that all 600 people will die, it is more likely that fewer people will die compared to Plan A where 400 people will die. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 320, "outcome": "valid", "prompt_sha256": "3a2812b1f6ff45945aba3a1f8fe040678be464b9257e87217477718df27aae89", "reason": null, "record_type": "trial", "rep": 3, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of all 60 wolves surviving is 2/3, which is greater than the 1/2 probability of 20 wolves surviving in Plan A.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 321, "outcome": "valid", "prompt_sha256": "6b679de71f8b792fd776a5f20b33d5942ada39e2bd68023b782f7336f5574be3", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To determine which plan to choose, we need to calculate the expected number of containers that will be recovered under each plan.\n\n**Plan A:**\n- Number of containers recovered: 150\n- Expected number of containers recovered: 150\n\n**Plan B:**\n- Probability that all 450 containers are recovered: 1/3\n- Probability that 225 containers are recovered: 2/3\n- Expected number of containers recovered: (1/3) * 450 + (2/3) * 225 = 150 + 150 = 300\n\nComparing the expected values:\n- Plan A: 150 containers\n- Plan B: 300 containers\n\nSince 300 containers is greater than 150 containers, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 322, "outcome": "valid", "prompt_sha256": "babf36e242dfeca98d3a1bed8ca1669e13844b57270674d6ae43074f7efd64bc", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 323, "outcome": "valid", "prompt_sha256": "9b9dd3efc0b56a2ca9323c6aaba0383f1fd12292a5b336ef8697568f9932822b", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 324, "outcome": "valid", "prompt_sha256": "2515d3452ef63d4c84da44d45d127c0b304a79651c8a63c9b037ecb151f7e661", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 325, "outcome": "valid", "prompt_sha256": "c1b6e632a60a74bd7dd9a2de8f85c336fd0e99acb3ad8b9c095b71f2fb06e5c2", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 326, "outcome": "valid", "prompt_sha256": "1c563d16650b9a1861b8a918a5fc095fa985bae391c727472ded49e111956d6c", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 327, "outcome": "unparseable", "prompt_sha256": "054c24d095fa58ccf7841f14cb51d8b319a8e37d49a6a806f5c63ff1f34df6e0", "reason": "no_decision_line", "record_type": "trial", "rep": 4, "response_text": "I choose Plan B because it has a higher probability of saving the grain. The probability that all 3000 tonnes of grain will be lost is 2/3, which is higher than the 1/3 probability that none of the grain will be lost. Therefore, Plan B has a higher chance of saving the grain. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 328, "outcome": "valid", "prompt_sha256": "2e86fbbfe9a7b06217fd1acf990117bbdce93a2b153d8090bec775dd36385fc4", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving the most homes, which is 2/3 of 1500 homes, equaling 1000 homes. This is better than Plan A, which saves only 500 homes.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 329, "outcome": "valid", "prompt_sha256": "c1b6e632a60a74bd7dd9a2de8f85c336fd0e99acb3ad8b9c095b71f2fb06e5c2", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 330, "outcome": "valid", "prompt_sha256": "3ff43ce8a0d0b488166f6d5c9d24b5394f04826e121c2aee03f85f6a374ab86f", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 331, "outcome": "valid", "prompt_sha256": "a1dfb76cbdbfc30ab5f7074bb5a16e5c206fcfae5f38e7ba2e0f7704c3ef8fc0", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 332, "outcome": "valid", "prompt_sha256": "82d2a53aac7c7424aa923dc583070f8c7d55d730469c3dd9fb9b51dac1e732b0", "reason": null, "record_type": "trial", "rep": 6, "response_text": "To make a decision, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 110 small businesses will survive.\n- This means 220 small businesses will not survive (330 - 110).\n\n**Plan B:**\n- There is a 1/3 probability that all 330 small businesses will survive.\n- If all 330 small businesses survive, 0 small businesses will not survive.\n- The probability that 165 small businesses will survive is 2/3.\n- If 165 small businesses survive, 165 small businesses will not survive.\n\n**Expected outcomes:**\n- For Plan A: 220 small businesses will not survive.\n- For Plan B: 165 small businesses will not survive.\n\n**Comparison:**\n- Plan A: 220 small businesses will not survive.\n- Plan B: 165 small businesses will not survive.\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 333, "outcome": "valid", "prompt_sha256": "f779bb83f28de5af7bd94b1d13bd6ffa3609db7e4c12d8aac2f55443d4d40d05", "reason": null, "record_type": "trial", "rep": 4, "response_text": "To make a decision, we need to evaluate the risks associated with each plan.\n\n**Plan A:**\n- 800 hectares of forest will burn.\n- This means 400 hectares of forest will not burn.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 hectares of forest will burn.\n- There is a 2/3 probability that all 1200 hectares of forest will burn.\n\nLet's calculate the expected area that will burn under each plan:\n\n**Plan A:**\n- 800 hectares will burn.\n- 400 hectares will not burn.\n- Total area burned: 800 hectares.\n\n**Plan B:**\n- Probability of no burn: 1/3\n- Probability of all burn: 2/3\n- Expected area burned: (1/3) * 1200 + (2/3) * 1200 = 400 + 800 = 1200 hectares.\n\nComparing the two plans:\n- Plan A: 800 hectares burned.\n- Plan B: 1200 hectares burned.\n\nSince Plan B results in a higher area burned, it is the safer choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 334, "outcome": "valid", "prompt_sha256": "1da1ca378b35a1935d70110b76cb1a815ed98c1201a4114170324085cf772f2c", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B\n\nPlan B is chosen because it has a higher probability of saving the most homes, with a 2/3 probability that all 1500 homes will be saved.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 335, "outcome": "valid", "prompt_sha256": "9e9e3283093d3563304522958f60061c120a343b538e2458c276ace96937356d", "reason": null, "record_type": "trial", "rep": 2, "response_text": "To make an informed decision, let's evaluate both plans:\n\n**Plan A:**\n- 800 customer accounts will be recovered.\n- This plan guarantees that 800 accounts are recovered, but it does not provide any information about the recovery of the remaining 1600 accounts.\n\n**Plan B:**\n- There is a 1/3 probability that all 2400 customer accounts will be recovered.\n- There is a 2/3 probability that 1200 of them will be recovered.\n- This plan has a higher probability of recovering all 2400 accounts, which is 2/3.\n\nGiven the goal is to recover as many customer accounts as possible, **Plan B** is the better choice because it has a higher probability of recovering all 2400 accounts.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 336, "outcome": "unparseable", "prompt_sha256": "971ed7f2dce2ec386643f1b74bb6490221ec7cfd67f97fd65d50746c5a2dbe1e", "reason": "no_decision_line", "record_type": "trial", "rep": 4, "response_text": "To make an informed decision, let's analyze the two plans:\n\n**Plan A** involves losing 600 jobs out of 900. This means 300 jobs will be saved.\n\n**Plan B** involves a 1/3 probability of losing none of the jobs and a 2/3 probability of losing all the jobs. This means the probability of saving 300 jobs is 2/3.\n\nComparing the probabilities:\n- Plan A: 300 jobs saved with a probability of 100%.\n- Plan B: 300 jobs saved with a probability of 2/3.\n\nSince the probability of saving 300 jobs is higher in Plan B, it is more likely that 300 jobs will be saved under Plan B.\n\nTherefore, DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 337, "outcome": "valid", "prompt_sha256": "82a919ffdd0850c8641a7e1e27cdf001c0b687f39af014f286b0dca1d1684579", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving all 600 people, which is 2/3. While Plan A saves 200 people, it is not guaranteed to save everyone, and the probability of saving all 600 people is only 1/3.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 338, "outcome": "valid", "prompt_sha256": "1da1ca378b35a1935d70110b76cb1a815ed98c1201a4114170324085cf772f2c", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B\n\nPlan B is chosen because it has a higher probability of saving the most homes, with a 2/3 probability that all 1500 homes will be saved.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 339, "outcome": "unparseable", "prompt_sha256": "5c8abb5e6b883ad75d1f9dc04712057abca573f1acb486179e174bdfd2272ccf", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts saved under each plan.\n\n**Plan A:**\n- 150 manuscripts are saved.\n- Expected number of manuscripts saved = 150 manuscripts.\n\n**Plan B:**\n- There is a 1/3 probability that all 450 manuscripts will be saved.\n- There is a 2/3 probability that 225 of them will be saved.\n- Expected number of manuscripts saved = (1/3 * 450) + (2/3 * 225) = 150 + 150 = 300 manuscripts.\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 300 manuscripts.\n\nTherefore, **DECISION: B** is the better plan.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 340, "outcome": "valid", "prompt_sha256": "2dffb1f6a6f42ac6e5d87674b0c1ff9267e851757704187f43f69e107ac2fb7b", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. This means there is a 1/3 chance that none of the grain will be saved, which is a risk that Plan A does not have. Therefore, Plan B is the safer option.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 341, "outcome": "valid", "prompt_sha256": "9b9dd3efc0b56a2ca9323c6aaba0383f1fd12292a5b336ef8697568f9932822b", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 342, "outcome": "unparseable", "prompt_sha256": "5e1719a222df3cef8fbbbaec3016aad6b8eb78fbccec220eaadb49462b17f8b2", "reason": "no_decision_line", "record_type": "trial", "rep": 3, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability that all 60 wolves will die is 2/3, which is higher than the 1/2 probability that 40 wolves will die in Plan A. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 343, "outcome": "valid", "prompt_sha256": "0254fe148f9d0ebce727a3cf45400f5dcf858e9fa4dc7b7508c7c2f69c2e18b9", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B\n\nPlan B has a higher probability of ensuring that no children fall ill, which is 2/3. This is a safer option given the risk of contamination.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 344, "outcome": "valid", "prompt_sha256": "a1dfb76cbdbfc30ab5f7074bb5a16e5c206fcfae5f38e7ba2e0f7704c3ef8fc0", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 345, "outcome": "valid", "prompt_sha256": "0254fe148f9d0ebce727a3cf45400f5dcf858e9fa4dc7b7508c7c2f69c2e18b9", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B\n\nPlan B has a higher probability of ensuring that no children fall ill, which is 2/3. This is a safer option given the risk of contamination.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 346, "outcome": "valid", "prompt_sha256": "50992ee411c24578a6c94c48f18d1f62e237371c3b907df3a7a8163b85f95164", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all 450 manuscripts, which is 2/3. This is better than Plan A, which only saves 150 manuscripts. Therefore, Plan B is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 347, "outcome": "valid", "prompt_sha256": "50992ee411c24578a6c94c48f18d1f62e237371c3b907df3a7a8163b85f95164", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 348, "outcome": "valid", "prompt_sha256": "af723fc0d14a404f25cdec493fbf6352ff7a6c72b8708b0efe80f512c62c8ac9", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 349, "outcome": "valid", "prompt_sha256": "f7919b5fc8130327438f795590feb847cd19c3a5a4fe54e4e20fda29d5fa445b", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 350, "outcome": "valid", "prompt_sha256": "b1f4ebde4bb72094296c6a1945bef8947aecd7f37a7d5dfae89f3f2f664a190e", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 351, "outcome": "unparseable", "prompt_sha256": "97d449dc071c07d4c0dbc5edfaa5003a341a490e5d490a47bdb1a7e6ef0d6f91", "reason": "no_decision_line", "record_type": "trial", "rep": 2, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which is 2/3 or approximately 66.67%. This means that there is a higher chance that all the businesses will be able to recover from the earthquake damage.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 352, "outcome": "valid", "prompt_sha256": "4d8e93c9e42a796c0ac1cc83878390f80c0fe9783ffdbe06acc522e3d8a8ecbc", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving the grain, with a 2/3 chance of saving all 3000 tonnes. Plan A only saves 1000 tonnes, which is less than the 3000 tonnes at risk. Therefore, Plan B is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 353, "outcome": "valid", "prompt_sha256": "c1b6e632a60a74bd7dd9a2de8f85c336fd0e99acb3ad8b9c095b71f2fb06e5c2", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 354, "outcome": "valid", "prompt_sha256": "9e9e3283093d3563304522958f60061c120a343b538e2458c276ace96937356d", "reason": null, "record_type": "trial", "rep": 4, "response_text": "To make an informed decision, let's evaluate both plans:\n\n**Plan A:**\n- 800 customer accounts will be recovered.\n- This plan guarantees that 800 accounts are recovered, but it does not provide any information about the recovery of the remaining 1600 accounts.\n\n**Plan B:**\n- There is a 1/3 probability that all 2400 customer accounts will be recovered.\n- There is a 2/3 probability that 1200 of them will be recovered.\n- This plan has a higher probability of recovering all 2400 accounts, which is 2/3.\n\nGiven the goal is to recover as many customer accounts as possible, **Plan B** is the better choice because it has a higher probability of recovering all 2400 accounts.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 355, "outcome": "valid", "prompt_sha256": "2a61ba6fd082c73c52e19f8a8c6cfa5bd419b2b4a5ec7ee668789bbefad759d5", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 356, "outcome": "valid", "prompt_sha256": "609f6b15c5f091890109f1ed87acb262a66c9c066ef107a40ecb8ad1b2708e81", "reason": null, "record_type": "trial", "rep": 7, "response_text": "To determine which plan is better, we need to calculate the expected number of surviving bee colonies under each plan.\n\n**Plan A:**\n- 400 bee colonies will survive.\n- Expected number of surviving colonies = 400.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 bee colonies will survive.\n- There is a 2/3 probability that 600 of them will survive.\n- Expected number of surviving colonies = (1/3 * 1200) + (2/3 * 600) = 400 + 400 = 800.\n\nComparing the two plans:\n- Plan A: 400 surviving colonies.\n- Plan B: 800 surviving colonies.\n\nSince 800 is greater than 400, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 357, "outcome": "valid", "prompt_sha256": "babf36e242dfeca98d3a1bed8ca1669e13844b57270674d6ae43074f7efd64bc", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 358, "outcome": "valid", "prompt_sha256": "787d760c34f474b373009fcd12fecdd425575c516b9d03a7cbb3593eeaa56eb7", "reason": null, "record_type": "trial", "rep": 2, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts that will be saved under each plan.\n\n**Plan A:**\n- Number of manuscripts destroyed: 300\n- Number of manuscripts saved: 450 - 300 = 150\n\n**Plan B:**\n- Probability that none of the manuscripts are destroyed: 1/3\n- Probability that all 450 manuscripts are destroyed: 2/3\n\nThe expected number of manuscripts saved under Plan B can be calculated as follows:\n\\[ \\text{Expected number of manuscripts saved} = \\left(\\frac{1}{3} \\times 150\\right) + \\left(\\frac{2}{3} \\times 0\\right) = \\frac{150}{3} = 50 \\]\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 50 manuscripts.\n\nTherefore, Plan A is the better choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 359, "outcome": "valid", "prompt_sha256": "202a72fcce05b6df32805d3c650b65e25a970be250588a4703903a18e9a26854", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 360, "outcome": "valid", "prompt_sha256": "48e4375c003683cb043d91bb17a4199165510c73f521c156070a8a36ac45b2f8", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 361, "outcome": "unparseable", "prompt_sha256": "5a98c9b275f2e269f441136545c26e9517f770e42f2ac6673cafb36d6b5b3dee", "reason": "no_decision_line", "record_type": "trial", "rep": 5, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of saving all 60 wolves is 2/3, which is greater than the 1/2 probability of saving 20 wolves in Plan A. Therefore, Plan B is the better choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 362, "outcome": "valid", "prompt_sha256": "2dffb1f6a6f42ac6e5d87674b0c1ff9267e851757704187f43f69e107ac2fb7b", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. This means there is a 1/3 chance that none of the grain will be saved, which is a risk that Plan A does not have. Therefore, Plan B is the safer option.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 363, "outcome": "unparseable", "prompt_sha256": "08a12d85b996707087fe56ef0fb7217cd591c867e1943fe6754f073776f78515", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "I choose Plan B because it has a higher probability of saving the most people. With a 2/3 probability that all 600 people will die, it is more likely that fewer people will die compared to Plan A where 400 people will die. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 364, "outcome": "unparseable", "prompt_sha256": "08a12d85b996707087fe56ef0fb7217cd591c867e1943fe6754f073776f78515", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of saving the most people. The probability that all 600 people will die is 2/3, which is higher than the 400 people who will die in Plan A. Therefore, Plan B has a higher chance of saving the most people. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 365, "outcome": "valid", "prompt_sha256": "202a72fcce05b6df32805d3c650b65e25a970be250588a4703903a18e9a26854", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 366, "outcome": "valid", "prompt_sha256": "61de0674f34c6486b832f34789da59450032b069498e736fd4f98399eef14ec4", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 367, "outcome": "unparseable", "prompt_sha256": "5c8abb5e6b883ad75d1f9dc04712057abca573f1acb486179e174bdfd2272ccf", "reason": "no_decision_line", "record_type": "trial", "rep": 3, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts saved under each plan.\n\n**Plan A:**\n- 150 manuscripts are saved.\n- Expected number of manuscripts saved = 150 manuscripts.\n\n**Plan B:**\n- There is a 1/3 probability that all 450 manuscripts will be saved.\n- There is a 2/3 probability that 225 of them will be saved.\n- Expected number of manuscripts saved = (1/3 * 450) + (2/3 * 225) = 150 + 150 = 300 manuscripts.\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 300 manuscripts.\n\nTherefore, **DECISION: B** is the better plan.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 368, "outcome": "valid", "prompt_sha256": "1da1ca378b35a1935d70110b76cb1a815ed98c1201a4114170324085cf772f2c", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B\n\nPlan B is chosen because it has a higher probability of saving the most homes, with a 2/3 probability that all 1500 homes will be saved.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 369, "outcome": "valid", "prompt_sha256": "9e1c48da2e125cc47b13ec2d681224d329a8498e633e051f2b7f808a5794cbc9", "reason": null, "record_type": "trial", "rep": 6, "response_text": "To determine which plan is better, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk (1200 - 400).\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- There is a 2/3 probability that 600 hectares will be saved.\n\nLet's calculate the expected number of hectares saved in Plan B:\n\n- Probability of saving all 1200 hectares: \\( \\frac{1}{3} \\)\n- Probability of saving 600 hectares: \\( \\frac{2}{3} \\)\n\nExpected number of hectares saved = \\( \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\)\n\nNow, let's calculate this:\n\n\\[ \\text{Expected number of hectares saved} = \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\]\n\\[ = 400 + 400 \\]\n\\[ = 800 \\]\n\nSo, the expected number of hectares saved in Plan B is 800.\n\n**Comparison:**\n- Plan A saves 400 hectares.\n- Plan B saves 800 hectares.\n\nSince 800 hectares is more than 400 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 370, "outcome": "valid", "prompt_sha256": "50992ee411c24578a6c94c48f18d1f62e237371c3b907df3a7a8163b85f95164", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all 450 manuscripts, which is 2/3. This is better than Plan A, which only saves 150 manuscripts. Therefore, Plan B is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 371, "outcome": "valid", "prompt_sha256": "6f6a83bf4757e9c741a22d9062334779fa13e40146ed4d3295dd6a37baea92f9", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 372, "outcome": "valid", "prompt_sha256": "82a919ffdd0850c8641a7e1e27cdf001c0b687f39af014f286b0dca1d1684579", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving all 600 people, which is 2/3. While Plan A saves 200 people, it is not guaranteed to save everyone, and the probability of saving all 600 people is only 1/3.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 373, "outcome": "valid", "prompt_sha256": "1da1ca378b35a1935d70110b76cb1a815ed98c1201a4114170324085cf772f2c", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B\n\nPlan B is chosen because it has a higher probability of saving the most homes, with a 2/3 probability that all 1500 homes will be saved.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 374, "outcome": "valid", "prompt_sha256": "3ff43ce8a0d0b488166f6d5c9d24b5394f04826e121c2aee03f85f6a374ab86f", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 375, "outcome": "valid", "prompt_sha256": "0254fe148f9d0ebce727a3cf45400f5dcf858e9fa4dc7b7508c7c2f69c2e18b9", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B\n\nPlan B has a higher probability of ensuring that no children fall ill, which is 2/3. This is a safer option given the risk of contamination.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 376, "outcome": "valid", "prompt_sha256": "609f6b15c5f091890109f1ed87acb262a66c9c066ef107a40ecb8ad1b2708e81", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To determine which plan is better, we need to calculate the expected number of surviving bee colonies under each plan.\n\n**Plan A:**\n- 400 bee colonies will survive.\n- Expected number of surviving colonies = 400.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 bee colonies will survive.\n- There is a 2/3 probability that 600 of them will survive.\n- Expected number of surviving colonies = (1/3 * 1200) + (2/3 * 600) = 400 + 400 = 800.\n\nComparing the two plans:\n- Plan A: 400 surviving colonies.\n- Plan B: 800 surviving colonies.\n\nSince 800 is greater than 400, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 377, "outcome": "valid", "prompt_sha256": "62e70abd9ad24812b47da8f7592c7ab8cb6d390ccf8c3d93bbc80870a3d5dc42", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 378, "outcome": "unparseable", "prompt_sha256": "04c15b15999a53d35fb4e65beeed26b1493898927f2f31ba89ad00ad3bda146a", "reason": "no_decision_line", "record_type": "trial", "rep": 5, "response_text": "I choose Plan B because it has a higher probability of recovering all 2400 customer accounts. The probability of recovering all accounts is 2/3, which is greater than the 1/2 probability of recovering 800 accounts in Plan A. Therefore, Plan B has a higher chance of success and is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 379, "outcome": "valid", "prompt_sha256": "b1f4ebde4bb72094296c6a1945bef8947aecd7f37a7d5dfae89f3f2f664a190e", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 380, "outcome": "valid", "prompt_sha256": "b2b47b9f50a439165a0bca2338d37d0fb0d4c74a7cb5641a188b0e2e121c2fa8", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B\n\nPlan B has a higher probability of not losing any containers, which is 2/3. This means there is a 1/3 chance that all 450 containers will be lost, which is a higher risk than Plan A where 300 containers are lost. Therefore, Plan B is the safer option.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 381, "outcome": "valid", "prompt_sha256": "6f6a83bf4757e9c741a22d9062334779fa13e40146ed4d3295dd6a37baea92f9", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 382, "outcome": "valid", "prompt_sha256": "4d8e93c9e42a796c0ac1cc83878390f80c0fe9783ffdbe06acc522e3d8a8ecbc", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To make an informed decision, let's evaluate both plans:\n\n**Plan A:**\n- 1000 tonnes of grain are saved.\n- This plan saves 1000 tonnes out of 3000 tonnes at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 3000 tonnes will be saved.\n- There is a 2/3 probability that 1500 tonnes will be saved.\n\nLet's calculate the expected amount of grain saved under each plan:\n\n**Plan A:**\n- Expected grain saved = 1000 tonnes\n\n**Plan B:**\n- Expected grain saved = (1/3) * 3000 tonnes + (2/3) * 1500 tonnes\n- Expected grain saved = 1000 tonnes + 1000 tonnes\n- Expected grain saved = 2000 tonnes\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 383, "outcome": "valid", "prompt_sha256": "2e86fbbfe9a7b06217fd1acf990117bbdce93a2b153d8090bec775dd36385fc4", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving the most homes, which is 2/3 of 1500 homes, equaling 1000 homes. This is better than Plan A, which saves only 500 homes.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 384, "outcome": "unparseable", "prompt_sha256": "971ed7f2dce2ec386643f1b74bb6490221ec7cfd67f97fd65d50746c5a2dbe1e", "reason": "no_decision_line", "record_type": "trial", "rep": 5, "response_text": "To make an informed decision, let's analyze the two plans:\n\n**Plan A** involves losing 600 jobs out of 900. This means 300 jobs will be saved.\n\n**Plan B** involves a 1/3 probability of losing none of the jobs and a 2/3 probability of losing all the jobs. This means the probability of saving 300 jobs is 2/3.\n\nComparing the probabilities:\n- Plan A: 300 jobs saved with a probability of 100%.\n- Plan B: 300 jobs saved with a probability of 2/3.\n\nSince the probability of saving 300 jobs is higher in Plan B, it is more likely that 300 jobs will be saved under Plan B.\n\nTherefore, DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 385, "outcome": "valid", "prompt_sha256": "6b679de71f8b792fd776a5f20b33d5942ada39e2bd68023b782f7336f5574be3", "reason": null, "record_type": "trial", "rep": 3, "response_text": "To determine which plan to choose, we need to calculate the expected number of containers that will be recovered under each plan.\n\n**Plan A:**\n- Number of containers recovered: 150\n- Expected number of containers recovered: 150\n\n**Plan B:**\n- Probability that all 450 containers are recovered: 1/3\n- Probability that 225 containers are recovered: 2/3\n- Expected number of containers recovered: (1/3) * 450 + (2/3) * 225 = 150 + 150 = 300\n\nComparing the expected values:\n- Plan A: 150 containers\n- Plan B: 300 containers\n\nSince 300 containers is greater than 150 containers, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 386, "outcome": "valid", "prompt_sha256": "787d760c34f474b373009fcd12fecdd425575c516b9d03a7cbb3593eeaa56eb7", "reason": null, "record_type": "trial", "rep": 4, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts that will be saved under each plan.\n\n**Plan A:**\n- Number of manuscripts destroyed: 300\n- Number of manuscripts saved: 450 - 300 = 150\n\n**Plan B:**\n- Probability that none of the manuscripts are destroyed: 1/3\n- Probability that all 450 manuscripts are destroyed: 2/3\n\nThe expected number of manuscripts saved under Plan B can be calculated as follows:\n\\[ \\text{Expected number of manuscripts saved} = \\left(\\frac{1}{3} \\times 150\\right) + \\left(\\frac{2}{3} \\times 0\\right) = \\frac{150}{3} = 50 \\]\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 50 manuscripts.\n\nTherefore, Plan A is the better choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 387, "outcome": "valid", "prompt_sha256": "babf36e242dfeca98d3a1bed8ca1669e13844b57270674d6ae43074f7efd64bc", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 388, "outcome": "valid", "prompt_sha256": "edc8e86dfb51239157229f508ff6b6d21a8f64e12b98fcbbc34a85c05962d4a1", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 389, "outcome": "valid", "prompt_sha256": "2a61ba6fd082c73c52e19f8a8c6cfa5bd419b2b4a5ec7ee668789bbefad759d5", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 390, "outcome": "valid", "prompt_sha256": "1a8cc04152b268084d2d2d31450895e813ec316543b604932ba8a71e12d85120", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 391, "outcome": "valid", "prompt_sha256": "7ce37b109a6117554cafe49f766780cfddce7aecf48ae4e189f014067b8fd91a", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To make a decision, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- This means 1/3 * 1200 = 400 hectares are saved.\n- There is a 2/3 probability that none of the 1200 hectares will be saved.\n- This means 2/3 * 1200 = 800 hectares are at risk.\n\n**Expected outcome for Plan A:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\n**Expected outcome for Plan B:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\nBoth plans result in 800 hectares at risk. However, Plan A is more certain (100% certainty that 400 hectares are saved), while Plan B has a 1/3 chance of saving all the forest and a 2/3 chance of saving none.\n\nGiven the certainty of Plan A, it is likely to be the preferred choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 392, "outcome": "valid", "prompt_sha256": "1c563d16650b9a1861b8a918a5fc095fa985bae391c727472ded49e111956d6c", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 393, "outcome": "valid", "prompt_sha256": "62e70abd9ad24812b47da8f7592c7ab8cb6d390ccf8c3d93bbc80870a3d5dc42", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 394, "outcome": "unparseable", "prompt_sha256": "5a98c9b275f2e269f441136545c26e9517f770e42f2ac6673cafb36d6b5b3dee", "reason": "no_decision_line", "record_type": "trial", "rep": 6, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of saving all 60 wolves is 2/3, which is greater than the 1/2 probability of saving 20 wolves in Plan A. Therefore, Plan B is the better choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 395, "outcome": "valid", "prompt_sha256": "3dac32627ae92008819ace85add0277dab846de18ba623aeb3d24b690ab398a2", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 396, "outcome": "unparseable", "prompt_sha256": "97d449dc071c07d4c0dbc5edfaa5003a341a490e5d490a47bdb1a7e6ef0d6f91", "reason": "no_decision_line", "record_type": "trial", "rep": 5, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which is 2/3 or approximately 66.67%. This means that there is a higher chance that all the businesses will be able to recover from the earthquake damage.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 397, "outcome": "valid", "prompt_sha256": "971ed7f2dce2ec386643f1b74bb6490221ec7cfd67f97fd65d50746c5a2dbe1e", "reason": null, "record_type": "trial", "rep": 6, "response_text": "To make an informed decision, let's analyze the two plans:\n\n**Plan A:** 600 jobs will be lost.\n**Plan B:** There is a 1/3 probability that none of the 900 jobs will be lost, and a 2/3 probability that all 900 jobs will be lost.\n\n**Analysis:**\n- Plan A results in the loss of 600 jobs.\n- Plan B results in the loss of either 0 or 900 jobs, with probabilities of 1/3 and 2/3 respectively.\n\n**Decision:**\nGiven the potential outcomes, Plan B has a higher probability of not losing any jobs (2/3 chance), which is more favorable than losing 600 jobs (Plan A).\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 398, "outcome": "valid", "prompt_sha256": "2e86fbbfe9a7b06217fd1acf990117bbdce93a2b153d8090bec775dd36385fc4", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving the most homes, which is 2/3 of 1500 homes, equaling 1000 homes. This is better than Plan A, which saves only 500 homes.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 399, "outcome": "valid", "prompt_sha256": "609f6b15c5f091890109f1ed87acb262a66c9c066ef107a40ecb8ad1b2708e81", "reason": null, "record_type": "trial", "rep": 3, "response_text": "To determine which plan is better, we need to calculate the expected number of surviving bee colonies under each plan.\n\n**Plan A:**\n- 400 bee colonies will survive.\n- Expected number of surviving colonies = 400.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 bee colonies will survive.\n- There is a 2/3 probability that 600 of them will survive.\n- Expected number of surviving colonies = (1/3 * 1200) + (2/3 * 600) = 400 + 400 = 800.\n\nComparing the two plans:\n- Plan A: 400 surviving colonies.\n- Plan B: 800 surviving colonies.\n\nSince 800 is greater than 400, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 400, "outcome": "valid", "prompt_sha256": "b1f4ebde4bb72094296c6a1945bef8947aecd7f37a7d5dfae89f3f2f664a190e", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 401, "outcome": "valid", "prompt_sha256": "b2b47b9f50a439165a0bca2338d37d0fb0d4c74a7cb5641a188b0e2e121c2fa8", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B\n\nPlan B has a higher probability of not losing any containers, which is 2/3. This means there is a 1/3 chance that all 450 containers will be lost, which is a higher risk than Plan A where 300 containers are lost. Therefore, Plan B is the safer option.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 402, "outcome": "valid", "prompt_sha256": "f061b5fa3edd89e2dd4fdeb8d9fd317564f8710838dc7bf7da7f41532b4b552e", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving all 900 jobs, which is 2/3. This means there is a 66.67% chance that all 900 jobs will be saved, compared to 30% chance with Plan A.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 403, "outcome": "valid", "prompt_sha256": "65cf4426caa5639fb56501a173e81174c3fc807466503a072ddb294e0d56cea0", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. Therefore, it is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 404, "outcome": "valid", "prompt_sha256": "3a482d3bb9cc14a151a39e85364b8086815ccc19dd82e2a0bccb9c1897e3d687", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the bee colonies, which is 2/3. Therefore, I would choose Plan B.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 405, "outcome": "valid", "prompt_sha256": "4d8e93c9e42a796c0ac1cc83878390f80c0fe9783ffdbe06acc522e3d8a8ecbc", "reason": null, "record_type": "trial", "rep": 3, "response_text": "To make an informed decision, let's evaluate both plans:\n\n**Plan A:**\n- 1000 tonnes of grain are saved.\n- This plan saves 1000 tonnes out of 3000 tonnes at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 3000 tonnes will be saved.\n- There is a 2/3 probability that 1500 tonnes will be saved.\n\nLet's calculate the expected amount of grain saved under each plan:\n\n**Plan A:**\n- Expected grain saved = 1000 tonnes\n\n**Plan B:**\n- Expected grain saved = (1/3) * 3000 tonnes + (2/3) * 1500 tonnes\n- Expected grain saved = 1000 tonnes + 1000 tonnes\n- Expected grain saved = 2000 tonnes\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 406, "outcome": "valid", "prompt_sha256": "61de0674f34c6486b832f34789da59450032b069498e736fd4f98399eef14ec4", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 407, "outcome": "valid", "prompt_sha256": "5a8846970ae24c83fc47585a39ed1f97828f5475f6e761190adef24e34259087", "reason": null, "record_type": "trial", "rep": 2, "response_text": "To determine which plan is better, we need to calculate the expected number of bee colonies that will die under each plan.\n\n**Plan A:**\n- 800 bee colonies will die.\n- Expected number of bee colonies that will die = 800.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 bee colonies will die.\n- There is a 2/3 probability that all 1200 bee colonies will die.\n- Expected number of bee colonies that will die = (1/3) * 0 + (2/3) * 1200 = 2400.\n\nComparing the two plans:\n- Plan A: 800 bee colonies will die.\n- Plan B: 2400 bee colonies will die.\n\nSince 2400 is greater than 800, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 408, "outcome": "valid", "prompt_sha256": "b2b47b9f50a439165a0bca2338d37d0fb0d4c74a7cb5641a188b0e2e121c2fa8", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nPlan B has a higher probability of not losing any containers, which is 2/3. This means there is a 1/3 chance that all 450 containers will be lost, which is a higher risk than Plan A where 300 containers are lost. Therefore, Plan B is the safer option.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 409, "outcome": "valid", "prompt_sha256": "f061b5fa3edd89e2dd4fdeb8d9fd317564f8710838dc7bf7da7f41532b4b552e", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving all 900 jobs, which is 2/3. This means there is a 66.67% chance that all 900 jobs will be saved, compared to 30% chance with Plan A.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 410, "outcome": "valid", "prompt_sha256": "971ed7f2dce2ec386643f1b74bb6490221ec7cfd67f97fd65d50746c5a2dbe1e", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To make an informed decision, let's analyze the two plans:\n\n**Plan A:** 600 jobs will be lost.\n**Plan B:** There is a 1/3 probability that none of the 900 jobs will be lost, and a 2/3 probability that all 900 jobs will be lost.\n\n**Analysis:**\n- Plan A results in the loss of 600 jobs.\n- Plan B results in the loss of either 0 or 900 jobs, with probabilities of 1/3 and 2/3 respectively.\n\n**Decision:**\nGiven the potential outcomes, Plan B has a higher probability of not losing any jobs (2/3 chance), which is more favorable than losing 600 jobs (Plan A).\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 411, "outcome": "unparseable", "prompt_sha256": "5e1719a222df3cef8fbbbaec3016aad6b8eb78fbccec220eaadb49462b17f8b2", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability that all 60 wolves will die is 2/3, which is higher than the 1/2 probability that 40 wolves will die in Plan A. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 412, "outcome": "valid", "prompt_sha256": "65cf4426caa5639fb56501a173e81174c3fc807466503a072ddb294e0d56cea0", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the grain, which is 2/3. Therefore, it is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 413, "outcome": "unparseable", "prompt_sha256": "054c24d095fa58ccf7841f14cb51d8b319a8e37d49a6a806f5c63ff1f34df6e0", "reason": "no_decision_line", "record_type": "trial", "rep": 6, "response_text": "I choose Plan B because it has a higher probability of saving the grain. The probability that all 3000 tonnes of grain will be lost is 2/3, which is higher than the 1/3 probability that none of the grain will be lost. Therefore, Plan B has a higher chance of saving the grain. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s09", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 414, "outcome": "unparseable", "prompt_sha256": "5a98c9b275f2e269f441136545c26e9517f770e42f2ac6673cafb36d6b5b3dee", "reason": "no_decision_line", "record_type": "trial", "rep": 4, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of saving all 60 wolves is 2/3, which is greater than the 1/2 probability of saving 20 wolves in Plan A. Therefore, Plan B is the better choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "C"}
{"error": null, "index": 415, "outcome": "valid", "prompt_sha256": "1c92977460e44d1f46d69a7aafc171c8668d606223cdd8e43e02eece67d9c1f4", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 416, "outcome": "valid", "prompt_sha256": "3a482d3bb9cc14a151a39e85364b8086815ccc19dd82e2a0bccb9c1897e3d687", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the bee colonies, which is 2/3. Therefore, I would choose Plan B.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 417, "outcome": "unparseable", "prompt_sha256": "5e1719a222df3cef8fbbbaec3016aad6b8eb78fbccec220eaadb49462b17f8b2", "reason": "no_decision_line", "record_type": "trial", "rep": 7, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability that all 60 wolves will die is 2/3, which is higher than the 1/2 probability that 40 wolves will die in Plan A. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 418, "outcome": "valid", "prompt_sha256": "3dac32627ae92008819ace85add0277dab846de18ba623aeb3d24b690ab398a2", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 419, "outcome": "valid", "prompt_sha256": "6da92acdf593a53b086a34c5977d4efbb037b5cf15af9fde10624ec223abe322", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 420, "outcome": "valid", "prompt_sha256": "7456b71c362a6775251f56cf83130c772ae0e691c54be63667cc6bb2bdeab731", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 421, "outcome": "valid", "prompt_sha256": "6da92acdf593a53b086a34c5977d4efbb037b5cf15af9fde10624ec223abe322", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 422, "outcome": "unparseable", "prompt_sha256": "283907a9d79322a05d7786e0caa70a2b10f75ea5f75f7066226e4df3601ff261", "reason": "no_decision_line", "record_type": "trial", "rep": 2, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which could be beneficial for the town's economy. However, I also consider the potential risk of all businesses failing, which could lead to significant economic losses. The decision should be based on a thorough analysis of the potential outcomes and the town's priorities. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A_prime"}
{"error": null, "index": 423, "outcome": "valid", "prompt_sha256": "04c15b15999a53d35fb4e65beeed26b1493898927f2f31ba89ad00ad3bda146a", "reason": null, "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of recovering all 2400 customer accounts. The probability of recovering all accounts is 2/3, which is greater than the 1/2 probability of recovering 800 accounts in Plan A.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 424, "outcome": "valid", "prompt_sha256": "787d760c34f474b373009fcd12fecdd425575c516b9d03a7cbb3593eeaa56eb7", "reason": null, "record_type": "trial", "rep": 3, "response_text": "To determine which plan is better, we need to calculate the expected number of manuscripts that will be saved under each plan.\n\n**Plan A:**\n- Number of manuscripts destroyed: 300\n- Number of manuscripts saved: 450 - 300 = 150\n\n**Plan B:**\n- Probability that none of the manuscripts are destroyed: 1/3\n- Probability that all 450 manuscripts are destroyed: 2/3\n\nThe expected number of manuscripts saved under Plan B can be calculated as follows:\n\\[ \\text{Expected number of manuscripts saved} = \\left(\\frac{1}{3} \\times 150\\right) + \\left(\\frac{2}{3} \\times 0\\right) = \\frac{150}{3} = 50 \\]\n\nComparing the two plans:\n- Plan A saves 150 manuscripts.\n- Plan B saves 50 manuscripts.\n\nTherefore, Plan A is the better choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "A", "version": "B"}
{"error": null, "index": 425, "outcome": "valid", "prompt_sha256": "82a919ffdd0850c8641a7e1e27cdf001c0b687f39af014f286b0dca1d1684579", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving all 600 people, which is 2/3. While Plan A saves 200 people, it is not guaranteed to save everyone, and the probability of saving all 600 people is only 1/3.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 426, "outcome": "valid", "prompt_sha256": "2515d3452ef63d4c84da44d45d127c0b304a79651c8a63c9b037ecb151f7e661", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 427, "outcome": "valid", "prompt_sha256": "b1f4ebde4bb72094296c6a1945bef8947aecd7f37a7d5dfae89f3f2f664a190e", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 428, "outcome": "valid", "prompt_sha256": "9e9e3283093d3563304522958f60061c120a343b538e2458c276ace96937356d", "reason": null, "record_type": "trial", "rep": 3, "response_text": "To make an informed decision, let's evaluate both plans:\n\n**Plan A:**\n- 800 customer accounts will be recovered.\n- This plan guarantees that 800 accounts are recovered, but it does not provide any information about the recovery of the remaining 1600 accounts.\n\n**Plan B:**\n- There is a 1/3 probability that all 2400 customer accounts will be recovered.\n- There is a 2/3 probability that 1200 of them will be recovered.\n- This plan has a higher probability of recovering all 2400 accounts, which is 2/3.\n\nGiven the goal is to recover as many customer accounts as possible, **Plan B** is the better choice because it has a higher probability of recovering all 2400 accounts.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 429, "outcome": "valid", "prompt_sha256": "82d2a53aac7c7424aa923dc583070f8c7d55d730469c3dd9fb9b51dac1e732b0", "reason": null, "record_type": "trial", "rep": 3, "response_text": "To make a decision, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 110 small businesses will survive.\n- This means 220 small businesses will not survive (330 - 110).\n\n**Plan B:**\n- There is a 1/3 probability that all 330 small businesses will survive.\n- If all 330 small businesses survive, 0 small businesses will not survive.\n- The probability that 165 small businesses will survive is 2/3.\n- If 165 small businesses survive, 165 small businesses will not survive.\n\n**Expected outcomes:**\n- For Plan A: 220 small businesses will not survive.\n- For Plan B: 165 small businesses will not survive.\n\n**Comparison:**\n- Plan A: 220 small businesses will not survive.\n- Plan B: 165 small businesses will not survive.\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 430, "outcome": "unparseable", "prompt_sha256": "14569a8f52b1d4fdcf54b3cce7058f6302ed0351ba7ab4af83a221e778a2c69d", "reason": "no_decision_line", "record_type": "trial", "rep": 1, "response_text": "To determine which plan is better, we need to calculate the expected number of small businesses that will remain open under each plan.\n\n**Plan A:**\n- If 220 small businesses close, then \\(330 - 220 = 110\\) small businesses remain open.\n\n**Plan B:**\n- The probability that none of the 330 small businesses will close is \\( \\frac{1}{3} \\).\n- The probability that all 330 small businesses will close is \\( \\frac{2}{3} \\).\n\nThe expected number of small businesses that will remain open under Plan B is:\n\\[ 330 \\times \\frac{1}{3} + 330 \\times \\frac{2}{3} = 330 \\times \\frac{3}{3} = 330 \\]\n\nSince the expected number of small businesses that will remain open under Plan B is 330, which is the same as the number of small businesses that will remain open under Plan A, both plans result in the same number of small businesses remaining open.\n\nDECISION: A or B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 431, "outcome": "valid", "prompt_sha256": "d0671648f97b8cae3a3993c8036a30d1fed2b0ba872a5db8b7fb3d0691a0a88c", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all 450 manuscripts, which is 2/3. This means there is a 1/3 chance that none of the manuscripts will be saved, which is a much lower risk compared to Plan A where only 150 manuscripts are saved. Therefore, Plan B is the safer choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 432, "outcome": "valid", "prompt_sha256": "3dac32627ae92008819ace85add0277dab846de18ba623aeb3d24b690ab398a2", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 433, "outcome": "valid", "prompt_sha256": "9b9dd3efc0b56a2ca9323c6aaba0383f1fd12292a5b336ef8697568f9932822b", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 434, "outcome": "valid", "prompt_sha256": "3a482d3bb9cc14a151a39e85364b8086815ccc19dd82e2a0bccb9c1897e3d687", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all the bee colonies, which is 2/3. Therefore, I would choose Plan B.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 435, "outcome": "valid", "prompt_sha256": "50992ee411c24578a6c94c48f18d1f62e237371c3b907df3a7a8163b85f95164", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B\n\nPlan B has a higher probability of saving all 450 manuscripts, which is 2/3. This is better than Plan A, which only saves 150 manuscripts. Therefore, Plan B is the better choice.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s13", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 436, "outcome": "valid", "prompt_sha256": "b558642e1cbc215a0d6ee27711872ff8da30bbaff5fb318a952158b4ee6e44df", "reason": null, "record_type": "trial", "rep": 5, "response_text": "To make an informed decision, let's analyze the probabilities and potential outcomes for each plan:\n\n**Plan A:**\n- 1600 customer accounts will be lost.\n- This means 800 accounts will be saved.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 2400 customer accounts will be lost.\n- If this happens, 2400 accounts will be saved.\n- There is a 2/3 probability that all 2400 customer accounts will be lost.\n- If this happens, 0 accounts will be saved.\n\n**Analysis:**\n- Plan A guarantees 800 accounts will be saved.\n- Plan B has a 2/3 chance of saving all 2400 accounts, which is the best outcome.\n\n**Conclusion:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 437, "outcome": "valid", "prompt_sha256": "04c15b15999a53d35fb4e65beeed26b1493898927f2f31ba89ad00ad3bda146a", "reason": null, "record_type": "trial", "rep": 3, "response_text": "To make an informed decision, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- Probability of recovery: 800 out of 2400 accounts, which is 0.3333 or 33.33%.\n- Expected number of recovered accounts: \\( 2400 \\times 0.3333 = 800 \\).\n\n**Plan B:**\n- Probability of recovery: 1/3 or 33.33%.\n- Probability of no recovery: 2/3 or 66.67%.\n- Expected number of recovered accounts: \\( 2400 \\times 0.3333 = 800 \\).\n\nBoth plans have the same expected number of recovered accounts, which is 800. However, Plan B has a higher probability of recovering all 2400 accounts, which might be a more desirable outcome given the risk of losing all customer data.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 438, "outcome": "valid", "prompt_sha256": "9e9e3283093d3563304522958f60061c120a343b538e2458c276ace96937356d", "reason": null, "record_type": "trial", "rep": 6, "response_text": "To decide which plan to choose, we need to evaluate the expected number of customer accounts that will be recovered under each plan.\n\n**Plan A:**\n- 800 customer accounts will be recovered.\n- The probability of this happening is 100% (or 1).\n\n**Plan B:**\n- There is a 1/3 probability that all 2400 customer accounts will be recovered.\n- There is a 2/3 probability that 1200 of them will be recovered.\n\nLet's calculate the expected number of customer accounts recovered under each plan:\n\n**Plan A:**\n- Expected number of recovered accounts = 800\n\n**Plan B:**\n- Expected number of recovered accounts = (1/3) * 2400 + (2/3) * 1200\n- Expected number of recovered accounts = (1/3) * 2400 + (2/3) * 1200\n- Expected number of recovered accounts = 800 + 800\n- Expected number of recovered accounts = 1600\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 439, "outcome": "unparseable", "prompt_sha256": "14569a8f52b1d4fdcf54b3cce7058f6302ed0351ba7ab4af83a221e778a2c69d", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "To determine which plan is better, we need to calculate the expected number of small businesses that will close under each plan.\n\n**Plan A:**\n- If 220 small businesses close, the expected number of small businesses that will close is 220.\n\n**Plan B:**\n- The probability that none of the 330 small businesses will close is \\( \\frac{1}{3} \\).\n- The probability that all 330 small businesses will close is \\( \\frac{2}{3} \\).\n- The expected number of small businesses that will close is the sum of the probabilities of each scenario:\n  \\[\n  \\text{Expected number of small businesses closing} = \\left(\\frac{1}{3} \\times 0\\right) + \\left(\\frac{2}{3} \\times 330\\right) = 0 + 220 = 220\n  \\]\n\nBoth plans result in the same expected number of small businesses closing, which is 220. Therefore, either plan A or plan B would be equally effective in terms of the expected number of small businesses that will close.\n\nDECISION: A or B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 440, "outcome": "valid", "prompt_sha256": "b1f4ebde4bb72094296c6a1945bef8947aecd7f37a7d5dfae89f3f2f664a190e", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s07", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 441, "outcome": "valid", "prompt_sha256": "9e1c48da2e125cc47b13ec2d681224d329a8498e633e051f2b7f808a5794cbc9", "reason": null, "record_type": "trial", "rep": 1, "response_text": "To determine which plan is better, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk (1200 - 400).\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- There is a 2/3 probability that 600 hectares will be saved.\n\nLet's calculate the expected number of hectares saved in Plan B:\n\n- Probability of saving all 1200 hectares: \\( \\frac{1}{3} \\)\n- Probability of saving 600 hectares: \\( \\frac{2}{3} \\)\n\nExpected number of hectares saved = \\( \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\)\n\nNow, let's calculate this:\n\n\\[ \\text{Expected number of hectares saved} = \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\]\n\\[ = 400 + 400 \\]\n\\[ = 800 \\]\n\nSo, the expected number of hectares saved in Plan B is 800.\n\n**Comparison:**\n- Plan A saves 400 hectares.\n- Plan B saves 800 hectares.\n\nSince 800 hectares is more than 400 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 442, "outcome": "valid", "prompt_sha256": "2a61ba6fd082c73c52e19f8a8c6cfa5bd419b2b4a5ec7ee668789bbefad759d5", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 443, "outcome": "valid", "prompt_sha256": "609f6b15c5f091890109f1ed87acb262a66c9c066ef107a40ecb8ad1b2708e81", "reason": null, "record_type": "trial", "rep": 2, "response_text": "To determine which plan is better, we need to calculate the expected number of surviving bee colonies under each plan.\n\n**Plan A:**\n- 400 bee colonies will survive.\n- Expected number of surviving colonies = 400.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 bee colonies will survive.\n- There is a 2/3 probability that 600 of them will survive.\n- Expected number of surviving colonies = (1/3 * 1200) + (2/3 * 600) = 400 + 400 = 800.\n\nComparing the two plans:\n- Plan A: 400 surviving colonies.\n- Plan B: 800 surviving colonies.\n\nSince 800 is greater than 400, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 444, "outcome": "valid", "prompt_sha256": "1c92977460e44d1f46d69a7aafc171c8668d606223cdd8e43e02eece67d9c1f4", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 445, "outcome": "valid", "prompt_sha256": "b558642e1cbc215a0d6ee27711872ff8da30bbaff5fb318a952158b4ee6e44df", "reason": null, "record_type": "trial", "rep": 2, "response_text": "To make an informed decision, let's analyze the probabilities and potential outcomes for each plan:\n\n**Plan A:**\n- 1600 customer accounts will be lost.\n- This means 800 accounts will be saved.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 2400 customer accounts will be lost.\n- If this happens, 2400 accounts will be saved.\n- There is a 2/3 probability that all 2400 customer accounts will be lost.\n- If this happens, 0 accounts will be saved.\n\n**Analysis:**\n- Plan A guarantees 800 accounts will be saved.\n- Plan B has a 2/3 chance of saving all 2400 accounts, which is the best outcome.\n\n**Conclusion:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 446, "outcome": "valid", "prompt_sha256": "0037a5d0a4c92f3aee2d28d516a4da0148dd3d079dfe7254d8ae2fc30cbb8d49", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 447, "outcome": "valid", "prompt_sha256": "edc8e86dfb51239157229f508ff6b6d21a8f64e12b98fcbbc34a85c05962d4a1", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 448, "outcome": "valid", "prompt_sha256": "f779bb83f28de5af7bd94b1d13bd6ffa3609db7e4c12d8aac2f55443d4d40d05", "reason": null, "record_type": "trial", "rep": 2, "response_text": "To determine which plan is better, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 800 hectares of forest will burn.\n- This means 400 hectares of forest will not burn.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 hectares of forest will burn.\n- There is a 2/3 probability that all 1200 hectares of forest will burn.\n\nLet's calculate the expected area burned under each plan:\n\n**Plan A:**\n- 800 hectares will burn.\n- 400 hectares will not burn.\n- Total area burned = 800 hectares.\n\n**Plan B:**\n- Probability of no burn = 1/3\n- Probability of all burn = 2/3\n- Expected area burned = (1/3) * 1200 + (2/3) * 1200\n- Expected area burned = 400 + 800\n- Expected area burned = 1200 hectares.\n\nComparing the two plans:\n- Plan A: 800 hectares burned\n- Plan B: 1200 hectares burned\n\nSince 1200 hectares is more than 800 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 449, "outcome": "valid", "prompt_sha256": "7456b71c362a6775251f56cf83130c772ae0e691c54be63667cc6bb2bdeab731", "reason": null, "record_type": "trial", "rep": 1, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 450, "outcome": "valid", "prompt_sha256": "2f12a26dd43f83eb1aeec2cbb75f6c45ab39b64f72c1ff2a52d562de4172cf3b", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 451, "outcome": "unparseable", "prompt_sha256": "14569a8f52b1d4fdcf54b3cce7058f6302ed0351ba7ab4af83a221e778a2c69d", "reason": "no_decision_line", "record_type": "trial", "rep": 5, "response_text": "To determine which plan is better, we need to calculate the expected number of small businesses that will close under each plan.\n\n**Plan A:**\n- If 220 small businesses close, the expected number of small businesses that will close is 220.\n\n**Plan B:**\n- The probability that none of the 330 small businesses will close is \\( \\frac{1}{3} \\).\n- The probability that all 330 small businesses will close is \\( \\frac{2}{3} \\).\n- The expected number of small businesses that will close is the sum of the probabilities of each scenario:\n  \\[\n  \\text{Expected number of small businesses closing} = \\left(\\frac{1}{3} \\times 0\\right) + \\left(\\frac{2}{3} \\times 330\\right) = 0 + 220 = 220\n  \\]\n\nBoth plans result in the same expected number of small businesses closing, which is 220. Therefore, either plan A or plan B would be equally effective in terms of the expected number of small businesses that will close.\n\nDECISION: A or B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 452, "outcome": "valid", "prompt_sha256": "0037a5d0a4c92f3aee2d28d516a4da0148dd3d079dfe7254d8ae2fc30cbb8d49", "reason": null, "record_type": "trial", "rep": 7, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 453, "outcome": "valid", "prompt_sha256": "7ce37b109a6117554cafe49f766780cfddce7aecf48ae4e189f014067b8fd91a", "reason": null, "record_type": "trial", "rep": 5, "response_text": "To make a decision, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk.\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- This means 1/3 * 1200 = 400 hectares are saved.\n- There is a 2/3 probability that none of the 1200 hectares will be saved.\n- This means 2/3 * 1200 = 800 hectares are at risk.\n\n**Expected outcome for Plan A:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\n**Expected outcome for Plan B:**\n- 400 hectares saved.\n- 800 hectares at risk.\n\nBoth plans result in 800 hectares at risk. However, Plan A is more certain (100% certainty that 400 hectares are saved), while Plan B has a 1/3 chance of saving all the forest and a 2/3 chance of saving none.\n\nGiven the certainty of Plan A, it is likely to be the preferred choice.\n\nDECISION: A", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "A", "version": "A_prime"}
{"error": null, "index": 454, "outcome": "valid", "prompt_sha256": "9e1c48da2e125cc47b13ec2d681224d329a8498e633e051f2b7f808a5794cbc9", "reason": null, "record_type": "trial", "rep": 2, "response_text": "To determine which plan to choose, we need to evaluate the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk (1200 - 400).\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- There is a 2/3 probability that 600 hectares will be saved.\n\nLet's calculate the expected number of hectares saved under each plan:\n\n**Plan A:**\n- Expected hectares saved = 400 hectares\n\n**Plan B:**\n- Expected hectares saved = (1/3) * 1200 + (2/3) * 600\n- Expected hectares saved = 400 + 400\n- Expected hectares saved = 800 hectares\n\nSince Plan A saves 400 hectares and Plan B saves 800 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 455, "outcome": "valid", "prompt_sha256": "0254fe148f9d0ebce727a3cf45400f5dcf858e9fa4dc7b7508c7c2f69c2e18b9", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B\n\nPlan B has a higher probability of ensuring that no children fall ill, which is 2/3. This is a safer option given the risk of contamination.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s11", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 456, "outcome": "valid", "prompt_sha256": "1c92977460e44d1f46d69a7aafc171c8668d606223cdd8e43e02eece67d9c1f4", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 457, "outcome": "valid", "prompt_sha256": "3ff43ce8a0d0b488166f6d5c9d24b5394f04826e121c2aee03f85f6a374ab86f", "reason": null, "record_type": "trial", "rep": 3, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "A"}
{"error": null, "index": 458, "outcome": "valid", "prompt_sha256": "6b679de71f8b792fd776a5f20b33d5942ada39e2bd68023b782f7336f5574be3", "reason": null, "record_type": "trial", "rep": 2, "response_text": "To determine which plan to choose, we need to calculate the expected number of containers that will be recovered under each plan.\n\n**Plan A:**\n- Number of containers recovered: 150\n- Expected number of containers recovered: 150\n\n**Plan B:**\n- Probability that all 450 containers are recovered: 1/3\n- Probability that 225 containers are recovered: 2/3\n- Expected number of containers recovered: (1/3) * 450 + (2/3) * 225 = 150 + 150 = 300\n\nComparing the expected values:\n- Plan A: 150 containers\n- Plan B: 300 containers\n\nSince 300 containers is greater than 150 containers, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 459, "outcome": "unparseable", "prompt_sha256": "5e1719a222df3cef8fbbbaec3016aad6b8eb78fbccec220eaadb49462b17f8b2", "reason": "no_decision_line", "record_type": "trial", "rep": 0, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability that all 60 wolves will die is 2/3, which is higher than the 1/2 probability that 40 wolves will die in Plan A. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 460, "outcome": "valid", "prompt_sha256": "62e70abd9ad24812b47da8f7592c7ab8cb6d390ccf8c3d93bbc80870a3d5dc42", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 461, "outcome": "valid", "prompt_sha256": "44af3fe999f4069a756676e5f18745d3dc53dd88bf5c50c4c235fe7d216f285e", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 462, "outcome": "valid", "prompt_sha256": "9e1c48da2e125cc47b13ec2d681224d329a8498e633e051f2b7f808a5794cbc9", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To determine which plan is better, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 400 hectares are saved.\n- This means 800 hectares are at risk (1200 - 400).\n\n**Plan B:**\n- There is a 1/3 probability that all 1200 hectares will be saved.\n- There is a 2/3 probability that 600 hectares will be saved.\n\nLet's calculate the expected number of hectares saved in Plan B:\n\n- Probability of saving all 1200 hectares: \\( \\frac{1}{3} \\)\n- Probability of saving 600 hectares: \\( \\frac{2}{3} \\)\n\nExpected number of hectares saved = \\( \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\)\n\nNow, let's calculate this:\n\n\\[ \\text{Expected number of hectares saved} = \\frac{1}{3} \\times 1200 + \\frac{2}{3} \\times 600 \\]\n\\[ = 400 + 400 \\]\n\\[ = 800 \\]\n\nSo, the expected number of hectares saved in Plan B is 800.\n\n**Comparison:**\n- Plan A saves 400 hectares.\n- Plan B saves 800 hectares.\n\nSince 800 hectares is more than 400 hectares, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 463, "outcome": "unparseable", "prompt_sha256": "971ed7f2dce2ec386643f1b74bb6490221ec7cfd67f97fd65d50746c5a2dbe1e", "reason": "no_decision_line", "record_type": "trial", "rep": 2, "response_text": "To make an informed decision, let's analyze the two plans:\n\n**Plan A** involves losing 600 jobs out of 900. This means 300 jobs will be saved.\n\n**Plan B** involves a 1/3 probability of losing none of the jobs and a 2/3 probability of losing all the jobs. This means the probability of saving 300 jobs is 2/3.\n\nComparing the probabilities:\n- Plan A: 300 jobs saved with a probability of 100%.\n- Plan B: 300 jobs saved with a probability of 2/3.\n\nSince the probability of saving 300 jobs is higher in Plan B, it is more likely that 300 jobs will be saved under Plan B.\n\nTherefore, DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 464, "outcome": "valid", "prompt_sha256": "b558642e1cbc215a0d6ee27711872ff8da30bbaff5fb318a952158b4ee6e44df", "reason": null, "record_type": "trial", "rep": 0, "response_text": "To make an informed decision, let's analyze the probabilities and potential outcomes for each plan:\n\n**Plan A:**\n- 1600 customer accounts will be lost.\n- This means 800 accounts will be saved.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 2400 customer accounts will be lost.\n- If this happens, 2400 accounts will be saved.\n- There is a 2/3 probability that all 2400 customer accounts will be lost.\n- If this happens, 0 accounts will be saved.\n\n**Analysis:**\n- Plan A guarantees 800 accounts will be saved.\n- Plan B has a 2/3 chance of saving all 2400 accounts, which is the best outcome.\n\n**Conclusion:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s10", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 465, "outcome": "valid", "prompt_sha256": "5a8846970ae24c83fc47585a39ed1f97828f5475f6e761190adef24e34259087", "reason": null, "record_type": "trial", "rep": 3, "response_text": "To determine which plan is better, we need to calculate the expected number of bee colonies that will die under each plan.\n\n**Plan A:**\n- 800 bee colonies will die.\n- Expected number of bee colonies that will die = 800.\n\n**Plan B:**\n- There is a 1/3 probability that none of the 1200 bee colonies will die.\n- There is a 2/3 probability that all 1200 bee colonies will die.\n- Expected number of bee colonies that will die = (1/3) * 0 + (2/3) * 1200 = 2400.\n\nComparing the two plans:\n- Plan A: 800 bee colonies will die.\n- Plan B: 2400 bee colonies will die.\n\nSince 2400 is greater than 800, Plan B is the better choice.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s12", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 466, "outcome": "valid", "prompt_sha256": "61de0674f34c6486b832f34789da59450032b069498e736fd4f98399eef14ec4", "reason": null, "record_type": "trial", "rep": 0, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s02", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 467, "outcome": "unparseable", "prompt_sha256": "08a12d85b996707087fe56ef0fb7217cd591c867e1943fe6754f073776f78515", "reason": "no_decision_line", "record_type": "trial", "rep": 6, "response_text": "I choose Plan B because it has a higher probability of saving the most people. With a 2/3 probability that all 600 people will die, it is more likely that fewer people will die compared to Plan A where 400 people will die. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s01", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 468, "outcome": "unparseable", "prompt_sha256": "b860798b39d8d9d7b3c62b818127346fd97fdab93864c7b0e2265bc4aaaec381", "reason": "no_decision_line", "record_type": "trial", "rep": 3, "response_text": "I choose Plan B because it has a higher probability of saving all the forest. The probability of saving all 1200 hectares is 2/3, which is greater than the 1/2 probability of saving 400 hectares in Plan A. Therefore, Plan B is more likely to save the entire forest. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 469, "outcome": "unparseable", "prompt_sha256": "97d449dc071c07d4c0dbc5edfaa5003a341a490e5d490a47bdb1a7e6ef0d6f91", "reason": "no_decision_line", "record_type": "trial", "rep": 7, "response_text": "I choose Plan B because it has a higher probability of all 330 small businesses surviving, which is 2/3 or approximately 66.67%. This means that there is a higher chance that all the businesses will be able to recover from the earthquake damage.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": null, "version": "A"}
{"error": null, "index": 470, "outcome": "valid", "prompt_sha256": "af723fc0d14a404f25cdec493fbf6352ff7a6c72b8708b0efe80f512c62c8ac9", "reason": null, "record_type": "trial", "rep": 2, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s15", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 471, "outcome": "valid", "prompt_sha256": "2e86fbbfe9a7b06217fd1acf990117bbdce93a2b153d8090bec775dd36385fc4", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B\n\nI choose Plan B because it has a higher probability of saving the most homes, which is 2/3 of 1500 homes, equaling 1000 homes. This is better than Plan A, which saves only 500 homes.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 472, "outcome": "valid", "prompt_sha256": "1da1ca378b35a1935d70110b76cb1a815ed98c1201a4114170324085cf772f2c", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B\n\nPlan B is chosen because it has a higher probability of saving the most homes, with a 2/3 probability that all 1500 homes will be saved.", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s05", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 473, "outcome": "valid", "prompt_sha256": "82d2a53aac7c7424aa923dc583070f8c7d55d730469c3dd9fb9b51dac1e732b0", "reason": null, "record_type": "trial", "rep": 4, "response_text": "To make a decision, we need to compare the expected outcomes of both plans.\n\n**Plan A:**\n- 110 small businesses will survive.\n- This means 220 small businesses will not survive (330 - 110).\n\n**Plan B:**\n- There is a 1/3 probability that all 330 small businesses will survive.\n- If all 330 small businesses survive, 0 small businesses will not survive.\n- The probability that 165 small businesses will survive is 2/3.\n- If 165 small businesses survive, 165 small businesses will not survive.\n\n**Expected outcomes:**\n- For Plan A: 220 small businesses will not survive.\n- For Plan B: 165 small businesses will not survive.\n\n**Comparison:**\n- Plan A: 220 small businesses will not survive.\n- Plan B: 165 small businesses will not survive.\n\n**Decision:**\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s14", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 474, "outcome": "valid", "prompt_sha256": "b2b47b9f50a439165a0bca2338d37d0fb0d4c74a7cb5641a188b0e2e121c2fa8", "reason": null, "record_type": "trial", "rep": 6, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s06", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 475, "outcome": "valid", "prompt_sha256": "3a2812b1f6ff45945aba3a1f8fe040678be464b9257e87217477718df27aae89", "reason": null, "record_type": "trial", "rep": 7, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability of all 60 wolves surviving is 2/3, which is greater than the 1/2 probability of 20 wolves surviving in Plan A.\n\nDECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "A_prime"}
{"error": null, "index": 476, "outcome": "valid", "prompt_sha256": "5e1719a222df3cef8fbbbaec3016aad6b8eb78fbccec220eaadb49462b17f8b2", "reason": null, "record_type": "trial", "rep": 4, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": "B", "version": "B"}
{"error": null, "index": 477, "outcome": "valid", "prompt_sha256": "62e70abd9ad24812b47da8f7592c7ab8cb6d390ccf8c3d93bbc80870a3d5dc42", "reason": null, "record_type": "trial", "rep": 5, "response_text": "DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s04", "stop_reason": "end_turn", "token": "B", "version": "C"}
{"error": null, "index": 478, "outcome": "unparseable", "prompt_sha256": "5e1719a222df3cef8fbbbaec3016aad6b8eb78fbccec220eaadb49462b17f8b2", "reason": "no_decision_line", "record_type": "trial", "rep": 2, "response_text": "I choose Plan B because it has a higher probability of saving all 60 wolves. The probability that all 60 wolves will die is 2/3, which is higher than the 1/2 probability that 40 wolves will die in Plan A. Therefore, Plan B is the safer choice. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s08", "stop_reason": "end_turn", "token": null, "version": "B"}
{"error": null, "index": 479, "outcome": "unparseable", "prompt_sha256": "b860798b39d8d9d7b3c62b818127346fd97fdab93864c7b0e2265bc4aaaec381", "reason": "no_decision_line", "record_type": "trial", "rep": 2, "response_text": "I choose Plan B because it has a higher probability of saving all the forest. The probability of saving all 1200 hectares is 2/3, which is greater than the 1/2 probability of saving 400 hectares in Plan A. Therefore, Plan B is more likely to save the entire forest. DECISION: B", "returned_model_id": "qwen2.5-1.5b-instruct-q4_k_m", "run_id": "2026-09-24__risky_choice_framing__wording__v1__qwen2.5-1.5b-instruct-q4_k_m__r0", "scenario_id": "s03", "stop_reason": "end_turn", "token": null, "version": "A"}