Convergence to Bayesian Stable States without Assuming Observability
11 Pages Posted: 19 Feb 2024 Last revised: 12 Mar 2024
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: Suggested Citation