Bibliographic record
Abstract
Enmeshed in various social structures, humans must often weigh their own interest against the interest of others-including the common interest of groups they belong to. The Public Goods Game (PGG), which succinctly pits individual interest against group interest, has been a staple of research into how people make such decisions. It has been studied in many variations, in the laboratory and (increasingly) online. One of the defining parameters of the PGG is the marginal per capita return of the group project (MPCR), which determines the incentive for contributing to the group project relative to the incentive of keeping points in the personal account. The effect of MPCR on contributions has been investigated before, but its effects have never been characterised with high resolution. Here, we present a systematic and high-resolution investigation of the effect of MPCR in groups of three. We do this in a large-scale online decision making experiment recruiting participants from Amazon Mechanical Turk. Our results provide a fine-grained account of the relationship between incentive to cooperate on the one hand and cooperation on the other, and can help to provide a basis for choosing MPCR magnitudes for future research endeavours using online PGG studies.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".