We provide a framework for studying two-sided matching markets with incomplete information. The framework accommodates two-sided incomplete information as well as heterogeneous information among the agents. We propose a notion called stability for a market state, which, based upon agents'...

This paper studies a matching algorithm which is termed incomplete-information deferred acceptance (DA). We show that DA is finite and that it produces a stable market state. We also compare DA with algorithms in the literature, including the complete-information Gale-Shapley algorithm, the...

This paper studies a stability notion and matching processes in the job market with incomplete information on the workers' side. Each worker is associated with a type, and each firm cares about the type of her employee under a match. Moreover, firms' information structure is described by...

We introduce a new updating rule, the conditional maximum likelihood rule (CML) for updating ambiguous information. The CML formula replaces the likelihood term in Bayes’ rule with the maximal likelihood of the given signal conditional on the state. We show that CML satisfies a new axiom,...