Reputational and cooperative benefits of third-party compensation
Bibliographic record
Abstract
Although third-party punishment helps sustain group cooperation, might victim compensation provide third-parties with superior reputational benefits? Across 24 studies (N = 21,296), we provide a comprehensive examination of the consequences of the choice between punishment and compensation. What do people infer from, and how do they respond to, the choice of punishment versus compensation? Across various contexts ranging from economic games, to workplace injustice, to people’s own personal experience of witnessing third-party responses in their daily lives, we find that compensating victims leads to greater reputational and partner choice benefits relative to punishing perpetrators. In fact, even people who themselves prefer to punish still prefer social partners who compensate. We also find that the signal that is sent via third-party compensating is an honest signal—people who choose to compensate rather than punish score lower on measures of trait Machiavellianism, narcissism, and psychopathy. Furthermore, we find that the personal decision of whether to compensate or punish is influenced by both injunctive and descriptive norms. These findings provide an extensive analysis of the causes and consequences of third-party responding to moral violations.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".