Cooperation in social dilemmas: Free riding may be thwarted by second-order reward rather than by punishment.
Bibliographic record
Abstract
Cooperation among nonrelatives can be puzzling because cooperation often involves incurring costs to confer benefits on unrelated others. Punishment of noncooperators can sustain otherwise fragile cooperation, but the provision of punishment suffers from a "second-order" free-riding problem because nonpunishers can free ride on the benefits from costly punishment provided by others. One suggested solution to this problem is second-order punishment of nonpunishers; more generally, the threat or promise of higher order sanctions might maintain the lower order sanctions that enforce cooperation in collective action problems. Here the authors report on 3 experiments testing people's willingness to provide second-order sanctions by having participants play a cooperative game with opportunities to punish and reward each other. The authors found that people supported those who rewarded cooperators either by rewarding them or by punishing nonrewarders, but people did not support those who punished noncooperators--they did not reward punishers or punish nonpunishers. Furthermore, people did not approve of punishers more than they did nonpunishers, even when nonpunishers were clearly unwilling to use sanctions to support cooperation. The results suggest that people will much more readily support positive sanctions than they will support negative sanctions.
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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 | 0.004 | 0.017 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".