Extreme Outcomes Sway Risky Decisions from Experience
Bibliographic record
Abstract
ABSTRACT Whether buying stocks or playing the slots, people making real‐world risky decisions often rely on their experiences with the risks and rewards. These decisions, however, do not occur in isolation but are embedded in a rich context of other decisions, outcomes, and experiences. In this paper, we systematically evaluate how the local context of other rewarding outcomes alters risk preferences. Through a series of four experiments on decisions from experience, we provide evidence for an extreme‐outcome rule, whereby people overweight the most extreme outcomes (highest and lowest) in a given context. As a result, people should be more risk seeking for gains than losses, even with equally likely outcomes. Across the experiments, the decision context was varied so that the same outcomes served as the high extreme, low extreme, or neither. As predicted, people were more risk seeking for relative gains, but only when the risky option potentially led to the high‐extreme outcome. Similarly, people were more risk averse for relative losses, but only when the risky option potentially led to the low‐extreme outcome. We conclude that in risky decisions from experience, the biggest wins and the biggest losses seem to matter more than they should. Copyright © 2013 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".