Credible sets

A credible set is the set of stable states of an expectation satisfying internal and external stability as defined in Dutta and Vohra (2017) . We show that credible sets are characterized by two fundamental coalitional rationality axioms: basic coalitional rationality and farsighted rationality. Credible sets exist in a wide range of environments, including all finite games, and they provide a unifying framework for organizing prominent notions in the farsighted stability literature. Influential solution concepts such as the largest consistent set, the farsighted stable set, the (strong) rational expectations farsighted stable set, and the stable set when it does not suffer from the Harsanyi critique are all particular refinements of credible sets.

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Publication Details

Journal
Journal of Economic Theory
Published
2025-10-03
DOI
https://doi.org/10.1016/j.jet.2025.106098
Citations
1
Primary Topic
Bayesian Modeling and Causal Inference
Type
article
Field-Weighted Citation Impact
2.48
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article

Credible sets

Mert Kimya
1 citations
Journal of Economic Theory
Bayesian Modeling and Causal Inference
2.48
article

Credible sets

Mert Kimya
article en
1 citations

Abstract

A credible set is the set of stable states of an expectation satisfying internal and external stability as defined in Dutta and Vohra (2017) . We show that credible sets are characterized by two fundamental coalitional rationality axioms: basic coalitional rationality and farsighted rationality. Credible sets exist in a wide range of environments, including all finite games, and they provide a unifying framework for organizing prominent notions in the farsighted stability literature. Influential solution concepts such as the largest consistent set, the farsighted stable set, the (strong) rational expectations farsighted stable set, and the stable set when it does not suffer from the Harsanyi critique are all particular refinements of credible sets.

Journal of Economic TheoryVol. 230
The University of Sydney (AU)
Openalex Percentile: Top 8%
Bayesian Modeling and Causal Inference
2.48
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