Bibliographic record
Abstract
Abstract The purpose of this paper is to provide a new equilibrium concept of repeated games that involve incomplete information (rationalized subjective equilibrium) and a related framework (subjective game), and to analyse the relationships between players’ subjective images of games and the realized outcomes. We demonstrate the relationships among rationalized subjective equilibria, subjective equilibria, and original Nash equilibria by providing several examples. We also present several theorems analogous to the ordinary folk theorem and a simple application using the Cournot duopoly model. JEL Classification: C70, C72 Equilibres subjectifs rationalisés dans des jeux répétés L’objectif de cet article est de proposer un nouveau concept d’équilibre dans les jeux répétés où l’information est incomplète (équilibre subjectif rationalisé) et un cadre d’analyse approprié( jeu subjectif ), ainsi que d’analyser les rapports entre les images subjectives que les acteurs ont des jeux et de leurs résultats. On montre les rapports entre équilibres subjectifs rationalisés, équilibres subjectifs, et équilibres à la Nash à l’aide de divers exemples. On présente aussi plusieurs théorèmes analogues au « ordinary folk theorem » et une application simple utilisant le modèle de duopole de Cournot.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".