The Big Man Mechanism: how prestige fosters cooperation and creates prosocial leaders
Bibliographic record
Abstract
Anthropological evidence from diverse societies suggests that prestige-based leadership may provide a foundation for cooperation in many contexts. Here, inspired by such ethnographic observations and building on a foundation of existing research on the evolution of prestige, we develop a set of formal models to explore when an evolved prestige psychology might drive the cultural evolution of n-person cooperation, and how such a cultural evolutionary process might create novel selection pressures for genes that make prestigious individuals more prosocial. Our results reveal (i) how prestige can foster the cultural emergence of cooperation by generating correlated behavioural phenotypes, both between leaders and followers, and among followers; (ii) why, in the wake of cultural evolution, natural selection favours genes that make prestigious leaders more prosocial, but only when groups are relatively small; and (iii), why the effectiveness of status differences in generating cooperation in large groups depends on cultural transmission (and not primarily on deference or coercion). Our theoretical framework, and the specific predictions made by these models, sketch out an interdisciplinary research programme that cross-cuts anthropology, biology, psychology and economics. Some of our predictions find support from laboratory work in behavioural economics and are consistent with several real-world patterns.
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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 | 0.005 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".