Peer Information and Risk-taking under Competitive and Non-competitive Pay Schemes
Bibliographic record
Abstract
Incentive schemes that reward participants based on their relative performance are often thought to be particularly risk-inducing. Using a novel, real-effort task experiment in the laboratory, we find that the relationship between incentives and risk-taking is more nuanced and depends critically on the availability of information about peers' strategies and outcomes. Indeed, we find that when no peer information is available, relative rewards schemes are associated with significantly less risk-taking than non-competitive rewards. In contrast, when decision-makers receive information about their peers' actions and/or outcomes, relative incentive schemes are associated with more risk-taking than non-competitive schemes. The nature of the feedbackwhether subjects receive information about peers' strategies, outcomes, or both-also affects risk-taking. We find no evidence that competitors imitate their peers when they face only feedback about other subjects' risk-taking strategies. However, decision-makers take more risk when they see the gaps between their performance score and their peers' scores grow. Combined feedback about peers' strategies and performance-from which subjects may assess the overall relationship between risk-taking and success-is associated with more risk-taking when rewards are based on relative performance; we find no similar effect for non-competitive rewards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".