The Evolution of Ostracism in Human Societies
Bibliographic record
Abstract
Understanding how humans successfully stabilize public good contributions is a major ongoing question in the social and behavioral sciences. The use of targeted sanctions against defecting strategies is an important solution to this problem. However, ethnographic and behavioral evidence suggest that punishment is sometimes not used against defectors to stabilize cooperation. Sanctions instead are either light and insufficient to coerce cooperation or take the form of verbal repudiations, urging defectors to reform their behavior. Should defectors not reform, they are then ostracized from groups. We construct a cultural evolutionary game-theoretic model to study the evolution of ostracizing strategies in public goods games. We demonstrate that simple ostracizing strategies are unlikely to be evolutionarily viable and can neither encourage the evolution of contrite-defectors, who respond to punishment with cooperation, nor can invade recalcitrant-defecting populations, which ignore punishment. Motivated by the ethnographic literature, we then consider a hybrid sanctioning-ostracizing strategy that lightly-sanctions defectors before ostracizing repeat defectors. Such a strategy demonstrates clear advantages over simple sanctioning strategies. It can afford to impose light-sanctions when common because these sanctions are irrelevant when coercing future cooperation from defectors. More so, when recalcitrant defecting strategies have some possibility of arising in a population, sanctioning-ostracizing strategies dominate pure sanctioning ones, stabilizing cooperation with greater efficiency. Finally, our model makes psychological predictions concerning the reasoning processes that defectors will go through when defectors are coerced to cooperate by the threat of ostracism as opposed to sanctioning.
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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 | 0.005 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".