Social Conflict and the Evolution of Unequal Conventions
Bibliographic record
Abstract
Abstract We propose a theory of social norms (or conventions) that implement substantial levels of inequality between men and women, ethnic groups, and classes and that persist over long periods of time despite being inefficient and not supported by formal institutions. Consistent with historical cases, we extend the standard asymmetric stochastic evolutionary game model to allow subpopulation sizes to differ and idiosyncratic rejection of a status quo convention to be intentional to some degree (rather than purely random as in the standard evolutionary models). In this setting, if idiosyncratic play is sufficiently intentional and the subordinate class is sufficiently large relative to the elite, then risk-dominated conventions that are both more unequal and inefficient relative to alternative conventions will be stochastically stable and may persist for long periods. We show that the same is true in a general bipartite network of the population if most of the subordinate groups interactions are local, while the elite is more “cosmopolitan”. We apply the model to the evolution of wage conventions on the bipartite network of workers and employers, and find that an unequal monopsonistic wage convention is robust to the idiosyncratic play of workers that otherwise might displace it.
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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 | 0.007 | 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".