Expressed disapproval does not sustain long-term cooperation as effectively as costly punishment
Bibliographic record
Abstract
Punishment plays a role in human cooperation, but it is costly. Prior research shows that people are more cooperative when they expect to receive negative feedback for non-cooperation, even in the absence of costly punishment, which would have interesting implications for theory and applications. However, based on theories of habituation and cue-based learning, we propose that people will learn to ignore expressions of disapproval that are not clearly associated with material costs or benefits. To test this hypothesis, we conducted a between-subjects, 40-round public goods game (i.e. much longer than most studies), where participants could respond to others' contributions by sending numerical disapproval messages, paying to reduce others' earnings, or neither. Consistent with previous results, we observed steadily increasing contributions in the costly punishment condition. In contrast, contributions declined after the early rounds in the expressed disapproval condition, and were eventually no higher than the basic control condition with neither costly punishment nor disapproval ratings. In other words, costless disapproval may temporarily increase cooperation, but the effects fade. We discuss the theoretical and applied implications of our findings, including the unexpectedly high levels of cooperation in a second control condition.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".