We study the design of a decentralized two-sided matching market in which agents’ search is guided by the platform. There are finitely many agent types, each with (potentially random) preferences drawn from known type-specific distributions. Equipped with knowledge of these distributions, the...

We study dynamic matching in an infinite-horizon stochastic market. While all agents are potentially compatible with each other, some are hard-to-match and others are easy-to-match. Agents prefer to be matched as soon as possible and matches are formed either bilaterally or indirectly through...

We study the problem of optimal search (with costly information acquisition) for the best alternative among a pool of candidates that belong to different societal groups. Due to baked-in biases in the prior data, group-unaware comparisons of candidates may lead to unfair treatment of certain...