Designing Approximately Optimal Search on Matching Platforms

نویسندگان

چکیده

We study the design of a decentralized two-sided matching market in which agents’ search is guided by platform. There are finitely many agent types, each with (potentially random) preferences drawn from known type-specific distributions. Equipped knowledge these distributions, platform guides process determining meeting rate between pair types two sides. Focusing on symmetric pairwise continuum model, we first characterize unique stationary equilibrium that arises given feasible set rates. then introduce platform’s optimal directed problem, involves optimizing rates to maximize social welfare. show incentive issues arising congestion and cannibalization make problem fairly intricate. Nonetheless, develop an efficiently computable whose corresponding achieves at least [Formula: see text] welfare design. In fact, our construction always recovers first-best welfare, where incentives disregarded. Our simple easy implement, as its bipartite graph consists disjoint stars. Furthermore, implies can substantially limit choice yet induce approximately offer alternative designs improved approximation factors for markets certain special structures. Finally, likely best one hope establishing designing text]-hard even approximate beyond constant factor. This paper was accepted Omar Besbes, revenue management analytics. Supplemental Material: The online appendix available https://doi.org/10.1287/mnsc.2022.4601 .

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ژورنال

عنوان ژورنال: Management Science

سال: 2022

ISSN: ['0025-1909', '1526-5501']

DOI: https://doi.org/10.1287/mnsc.2022.4601