Sustaining Cooperation: Community Enforcement versus Specialized Enforcement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We introduce the possibility of coercive punishment by specialized enforcers into a model of community enforcement. We assume that, just as regular agents need to be given incentives to cooperate with each other, specialized enforcers need to be given incentives to carry out costly punishments. We fully characterize optimal equilibria in the model. When the specialized enforcement technology is sufficiently effective, cooperation is best sustained by a “one-time enforcer punishment equilibrium”, where any deviation by a regular agent is punished only once, and only by enforcers. In contrast, enforcers themselves are disciplined (at least in part) by community enforcement. The reason why there is no community enforcement following deviations by regular agents is that such a response, by reducing future cooperation, would decrease the amount of punishment that enforcers are willing to impose on deviators. Conversely, when the specialized enforcement technology is less effective, optimal equilibria involve a mix of specialized enforcement and community enforcement (which might take the form of “ostracism”). Our results hold both under perfect monitoring of actions and under various types of private monitoring.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it