Convergence to Bayesian Stable States without Assuming Observability

11 Pages Posted: 19 Feb 2024 Last revised: 12 Mar 2024

See all articles by Yi-Chun Chen

Yi-Chun Chen

National University of Singapore (NUS) - Department of Economics

Gaoji Hu

Shanghai University of Finance and Economics - School of Economics

Date Written: December 30, 2023

Abstract

Chen and Hu (2024) proposes a concept of Bayesian stability for matching with incomplete information. They also invoke the result of Chen and Hu (2020) to provide a foundation for their solution concept. Particularly, assuming in job matching that firms can observe the type of their employees, then starting from an arbitrary market state, a random learning-blocking path converges to a Bayesian stable state with probability one. In this companion note, we document how one could prove the following result without assuming observability, which strengthens Corollary 1 in Chen and Hu (2024).

Keywords: two-sided matching, Bayesian stability, convergence, observability

JEL Classification: C78, D40, D82, D83

Suggested Citation

Chen, Yi-Chun and Hu, Gaoji, Convergence to Bayesian Stable States without Assuming Observability (December 30, 2023). Available at SSRN: https://ssrn.com/abstract=4720596 or http://dx.doi.org/10.2139/ssrn.4720596

Yi-Chun Chen

National University of Singapore (NUS) - Department of Economics ( email )

1 Arts Link AS2 #06-02
Singapore 117570, Singapore 119077
Singapore

Gaoji Hu (Contact Author)

Shanghai University of Finance and Economics - School of Economics ( email )

777 Guoding Road
Shanghai, 200433
China

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