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Record W2017509081 · doi:10.1111/1540-5982.00009

Rationalized subjective equilibria in repeated games

2003· article· fr· W2017509081 on OpenAlexvenueno aff
Oishi Hidetsugu

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2003
Typearticle
Languagefr
FieldDecision Sciences
TopicGame Theory and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMathematical economicsCournot competitionSimple (philosophy)Nash equilibriumHumanitiesMathematicsEconomicsPhilosophyEpistemology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.231
GPT teacher head0.259
Teacher spread0.028 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2003
Admission routes1
Has abstractyes

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