Punishment Is Strongly Motivated by Revenge and Weakly Motivated by Inequity Aversion
Bibliographic record
Abstract
There are two broad functional explanations for second-party punishment: fitness-leveling and deterrence. The former suggests that people punish to reduce fitness differences, while the latter suggests that people punish in order to reciprocate losses and deter others from inflicting losses on them in the future. We explore the relative roles of these motivations using a pre-registered, two-player experiment with 2,426 US participants from Amazon Mechanical Turk. Participants played as the “responder” and were assigned to either a Take or Augment condition. In the Take condition, the “partner” could steal money from the responder’s bonus or do nothing. In the Augment condition, the partner could augment the responder’s bonus by giving them money at no cost to themselves or do nothing. We also manipulated the responders’ starting endowments, such that after the partner’s decision, responders experienced different payoff outcomes: advantageous inequity, equality, or varying degrees of disadvantageous inequity. Responders then decided whether to pay a cost to punish the partner. Punishment was clearly influenced by theft and was most frequent when theft resulted in disadvantageous inequity. However, people also punished in the absence of theft, particularly when confronted with disadvantageous inequity. While the effect of inequity on punishment was small, our results suggest that punishment is motivated by more than just the desire to reciprocate losses. These findings highlight the multiple motivations undergirding punishment and bear directly on functional explanations for the existence of punishment in human societies.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".