Steady steps versus sudden shifts: Cooperation in (a)symmetric linear and step-level social dilemmas
Bibliographic record
Abstract
Abstract Are groups of people better able to minimize a collective loss if there is a collective target that must be reached or if every small contribution helps? In this paper we investigate whether cooperation in social dilemmas can be increased by structuring the problem as a step-level social dilemma rather than a linear social dilemma and whether cooperation can be increased by manipulating endowment asymmetry between individuals. In two laboratory experiments using ‘Public Bad’ games, we found that that individuals defect less and are better able to minimize collective and personal costs in a step-level social dilemma than in a linear social dilemma. We found that the level of cooperation is not affected by an ambiguous threshold: even when participants cannot be sure about the optimal cooperation level, cooperation remains high in the step-level social dilemmas. We find mixed results for the effect of asymmetry on cooperation. These results imply that presenting social dilemmas as step-level games and reducing asymmetry can help solve environmental dilemmas in the long term.
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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 | 0.014 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".