Offloading punishment to karma: Thinking about karma reduces the punishment of transgressors
Bibliographic record
Abstract
Punishment and the threat thereof can help enforce social norms, but enacting punishment is often costly. To avoid these costs, individuals may prefer to offload the responsibility of punishment to others or to cultural institutions. We propose that shared beliefs about supernatural punishment contribute to minimizing the costs of interpersonal punishment by allowing people to offload punishment to supernatural entities. In a Third Party Punishment Game, we specifically test in a pre-registered experiment ( N = 1603 Americans and Singaporeans adults, recruited through Qualtrics' online panels) whether thinking about karma (a supernatural force that punishes misdeeds) reduces punishment. Results confirm that being prompted to consider karma reduces inclinations to punish selfishness in a Third Party Punishment Game. A second pre-registered study using a subtler prime of karma replicated this effect. These findings suggest that karma beliefs may have played a role in the cultural evolution of human cooperation by reducing the costs of human norm enforcement while maintaining incentives for prosocial behaviour through the threat of supernatural punishment.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".