Sustaining Cooperation: Community Enforcement versus Specialized Enforcement
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".