Fairness and signalling in bargaining games
Bibliographic record
Abstract
Abstract Cultural evolutionary models of bargaining can elucidate issues related to fairness and justice, and especially how fair and unfair conventions and norms might arise in human societies. One line of this research shows how the presence of social categories in such models creates inequitable equilibria that are not possible in models without social categories. This is taken to help explain why in human groups with social categories, inequity is the rule rather than the exception. But in previous models, it is typically assumed that these categories are rigid – in the sense that they cannot be altered, and easily observable – in the sense that all agents can identify each others’ category membership. In reality, social categories are not always so tidy. We introduce evolutionary models where the tags connected with social categories can be flexible, variable, or difficult to observe, i.e. where these tags can carry different amounts of information about group membership. We show how alterations to these tags can undermine the stability of unfair conventions. We argue that these results can inform projects intended to ameliorate inequity, especially projects that seek to alter the properties of tags by promoting experimentation, imitation, and play with identity markers.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".