Cheating and punishment in cooperative animal societies
Bibliographic record
Abstract
Cheaters-genotypes that gain a selective advantage by taking the benefits of the social contributions of others while avoiding the costs of cooperating-are thought to pose a major threat to the evolutionary stability of cooperative societies. In order for cheaters to undermine cooperation, cheating must be an adaptive strategy: cheaters must have higher fitness than cooperators, and their behaviour must reduce the fitness of their cooperative partners. It is frequently suggested that cheating is not adaptive because cooperators have evolved mechanisms to punish these behaviours, thereby reducing the fitness of selfish individuals. However, a simpler hypothesis is that such societies arise precisely because cooperative strategies have been favoured over selfish ones-hence, behaviours that have been interpreted as 'cheating' may not actually result in increased fitness, even when they go unpunished. Here, we review the empirical evidence for cheating behaviours in animal societies, including cooperatively breeding vertebrates and social insects, and we ask whether such behaviours are primarily limited by punishment. Our review suggests that both cheating and punishment are probably rarer than often supposed. Uncooperative individuals typically have lower, not higher, fitness than cooperators; and when evidence suggests that cheating may be adaptive, it is often limited by frequency-dependent selection rather than by punishment. When apparently punitive behaviours do occur, it remains an open question whether they evolved in order to limit cheating, or whether they arose before the evolution of cooperation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".