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Clearing Matching Markets Efficiently: Informative Signals and Match Recommendations

Author

Listed:
  • 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)

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.

Suggested Citation

  • Itai Ashlagi & Mark Braverman & Yash Kanoria & Peng Shi, 2020. "Clearing Matching Markets Efficiently: Informative Signals and Match Recommendations," Management Science, INFORMS, vol. 66(5), pages 2163-2193, May.
    • Handle: RePEc:inm:ormnsc:v:66:y:2020:i:5:p:2163-2193
    DOI: 10.1287/mnsc.2018.3265
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    References listed on IDEAS

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    2. Bó, Inácio & Hakimov, Rustamdjan, 2022. "The iterative deferred acceptance mechanism," Games and Economic Behavior, Elsevier, vol. 135(C), pages 411-433.
    3. Peng Shi, 2023. "Optimal Matchmaking Strategy in Two-Sided Marketplaces," Management Science, INFORMS, vol. 69(3), pages 1323-1340, March.
    4. Ni Huang & Gordon Burtch & Yumei He & Yili Hong, 2022. "Managing Congestion in a Matching Market via Demand Information Disclosure," Information Systems Research, INFORMS, vol. 33(4), pages 1196-1220, December.
    5. Maxwell Allman & Itai Ashlagi, 2023. "Interviewing Matching in Random Markets," Papers 2305.11350, arXiv.org, revised Sep 2023.
    6. Patel, Pankaj C. & Oghazi, Pejvak & Arunachalam, S., 2023. "Does consumer privacy act influence firm performance in the retail industry? Evidence from a US state-level law change," Journal of Business Research, Elsevier, vol. 162(C).
    7. Kenny Peng & Nikhil Garg, 2024. "Wisdom and Foolishness of Noisy Matching Markets," Papers 2402.16771, arXiv.org.
    8. Mylius, F., 2023. "Why Personal Ties (Still) Matter: Referrals and Congestion," Cambridge Working Papers in Economics 2356, Faculty of Economics, University of Cambridge.

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