Clearing Matching Markets Efficiently: Informative Signals and Match Recommendations

Itai Ashlagi (), Mark Braverman (), Yash Kanoria () and Peng Shi
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Itai Ashlagi: Department of Management Science and Engineering, Stanford University, Stanford, California 94305
Mark Braverman: Department of Computer Science, Princeton University, Princeton, New Jersey 08544
Yash Kanoria: Graduate Business School, Columbia University, New York, New York 10027
Peng Shi: Department of Data Sciences and Operations, Marshall School of Business, University of Southern California, Los Angeles, California 90007

Management Science, 2020, vol. 66, issue 5, 2163-2193

Abstract: We study how to reduce congestion in two-sided matching markets with private preferences. We measure congestion by the number of bits of information that agents must (i) learn about their own preferences, and (ii) communicate with others before obtaining their final match. Previous results suggest that a high level of congestion is inevitable under arbitrary preferences before the market can clear with a stable matching. We show that when the unobservable component of agent preferences satisfies certain natural assumptions, it is possible to recommend potential matches and encourage informative signals such that the market reaches a stable matching with a low level of congestion. Moreover, under our proposed approach, agents have negligible incentive to leave the marketplace or to look beyond the set of recommended partners. The intuitive idea is to only recommend partners with whom there is a nonnegligible chance that the agent will both like them and be liked by them. The recommendations are based on both the observable component of preferences and signals sent by agents on the other side that indicate interest.

Keywords: marketplace and platform design; communication complexity; stable matching; match recommendations; informative signaling (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (10)

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