Designing Approximately Optimal Search on Matching Platforms

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 platform guides the search process by determining the meeting rate between each pair of types from the two sides. Focusing on symmetric pairwise preferences in a continuum model, we first characterize the unique stationary equilibrium that arises given a feasible set of meeting rates. We then introduce the platform's optimal directed search problem, which involves optimizing meeting rates to maximize equilibrium social welfare. We first show that incentive issues arising from congestion and cannibalization make the design problem fairly intricate. Nonetheless, we develop an efficiently computable search design whose corresponding equilibrium achieves at least 1/4 the social welfare of the optimal design. In fact, our construction always recovers at least 1/4 the first-best social welfare, where agents' incentives are disregarded. Our search design is simple and easy to implement, as its corresponding bipartite graph consists of disjoint stars. Furthermore, our design implies the platform can substantially limit choice and yet induce an equilibrium with approximately optimal welfare. We offer alternative search designs with improved approximation factors for markets with certain special structures. Finally, we show that approximation is likely the best one can hope for by establishing that the problem of designing optimal directed search is NP-hard to even approximate beyond a certain constant factor.