Bibliographic record
Abstract
In this Article I explain why game theory has been so unsuccessful in accounting for the role of language in social interaction. I begin by exploring some of its most basic difficulties in this respect, in games of pure coordination, and trace these difficulties back to the most fundamental organizing concepts in the theory of games, namely, Nash equilibrium and common knowledge of rationality. Nash thinkers and Nash actors, I argue, are doomed to have very impoverished conversations as Nash talkers. The sorts of conversations they will have will leave them paralyzed in games of pure coordination and largely uncooperative in games where their interactions are at least partially characterized by conflicts of interest. These conversations are impoverished because they attempt to forge only a causal connection across the verbal exchanges between rational actors, not a conceptual one. What is needed is the richer sort of conversation that is idealized by law, that is, one where there is an interpenetration of concepts and commitments in the use of language between rational actors, the sort of thing we see under a truly shared or public reason. Law's reasonable thinkers, I argue, are more capable of coordinating, and law's reasonable talkers more capable of cooperating, than their Nash counterparts because, under objective reasonableness, they are committed to a more public conception of their conduct shaping what they do together.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".