Local competition sparks concerns for fairness in the ultimatum game
Bibliographic record
Abstract
Humans reject uneven divisions of resources, even at personal cost. This is observed in countless experiments using the ultimatum game, where a proposer offers to divide a resource with a responder who either accepts the division or rejects it (whereupon both earn zero). Researchers debate why humans evolved a psychology that is so averse to inequity within partnerships. We suggest that the scale of competition is crucial: under local competition with few competitors, individuals reject low offers, because they cannot afford to be disadvantaged relative to competitors. If one competes against the broader population (i.e. global competition), then it pays to accept low offers to increase one's absolute pay-off. We support this intuition with an illustrative game-theoretical model. We also conducted ultimatum games where participants received prizes based on pay-offs relative to immediate partners (local competition) versus a larger group (global competition). Participants demanded higher offers under local competition, suggesting that local competition increases people's demands for fairness and aversion to inequality.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".