Group and individual risk preferences: a lottery-choice experiment
Bibliographic record
Abstract
This paper focuses on decision making under risk, comparing group and individual risk preferences in a lottery-choice experiment inspired by Holt and Laury (2002). The experiment presents subjects with a menu of unordered lottery choices which allows us to measure risk aversion. In the individual treatment, subjects make lottery choices individually; in the group treatment, each subject was placed in an anonymous group of three, where unanimous lottery choice decisions were made via voting. Finally, in a third treatment, called the choice treatment, subjects could choose whether to be on their own or in a group. Our main findings are that groups are more likely than individuals to choose safe lotteries for decisions with low winning percentages. Moreover, groups converge toward less risky decisions because subjects who were relatively less risk averse were more likely to change their vote in order to conform to the group average decision; more risk-averse individuals were less likely to change their preferences. Finally our results reveal a positive relationship between preference for risk and willingness to decide alone.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".