Markets, Religion, Community Size, and the Evolution of Fairness and Punishment
Bibliographic record
Abstract
Large-scale societies in which strangers regularly engage in mutually beneficial transactions are puzzling. The evolutionary mechanisms associated with kinship and reciprocity, which underpin much of primate sociality, do not readily extend to large unrelated groups. Theory suggests that the evolution of such societies may have required norms and institutions that sustain fairness in ephemeral exchanges. If that is true, then engagement in larger-scale institutions, such as markets and world religions, should be associated with greater fairness, and larger communities should punish unfairness more. Using three behavioral experiments administered across 15 diverse populations, we show that market integration (measured as the percentage of purchased calories) positively covaries with fairness while community size positively covaries with punishment. Participation in a world religion is associated with fairness, although not across all measures. These results suggest that modern prosociality is not solely the product of an innate psychology, but also reflects norms and institutions that have emerged over the course of human history.
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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 | 0.002 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".