Public goods and bads with vulnerable individuals: How information and social nudges change behaviour
Bibliographic record
Abstract
Abstract In a diverse society, heterogeneous returns to public goods (PG) and public bads (PB) are more often the rule rather than the exception, and often the returns from the public pool are such that individuals who are most affected no longer have incentives to free ride on others. We consider this set‐up through a laboratory experiment and investigate how heterogeneity of marginal per capita returns (MPCRs) affect economic cooperation in both PG and PB games. We also examine whether information on heterogeneity—no information, information and information with a plea to help those who are most affected by the public pool—changes cooperation. Our results show that information regarding the heterogeneity does not change individual behaviour in both PG and PB games. However, a social plea to help individuals with MPCRs of 1.20 increases average group efficiency. Average individual contributions under the social plea treatment are either maintained or increased. Those with MPCRs of 1.20 are more cooperative than their counterparts but not as completely as theoretically predicted. The exact same individual is also more cooperative under a PG game than under a PB game; a result that remains unchanged whether MPCRs are homogeneous or heterogeneous.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".