Clearing Matching Markets Efficiently: Informative Signals and Match Recommendations

60 Pages Posted: 10 May 2017 Last revised: 18 Aug 2018

See all articles by Itai Ashlagi

Itai Ashlagi

Stanford University - Management Science & Engineering

Mark Braverman

Princeton University

Yash Kanoria

Columbia Business School - Decision Risk and Operations

Peng Shi

University of Southern California - Marshall School of Business

Date Written: August 17, 2018

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 by Segal (2007) and Gonczarowski et al. (2015) 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. This is desirable because the communication overhead is minimized while agents have negligible incentives to leave the marketplace or to look beyond the set of recommended partners. The main idea is to only recommend partners with whom the agent has a non-negligible chance of both liking and being liked by. The recommendations are based both on the observable component of preferences, and on the signals sent by agents on the other side that indicate interest.

Keywords: marketplace and platform design, communication complexity, stable matching, match recommendations, informative signaling

Suggested Citation

Ashlagi, Itai and Braverman, Mark and Kanoria, Yash and Shi, Peng, Clearing Matching Markets Efficiently: Informative Signals and Match Recommendations (August 17, 2018). Available at SSRN: https://ssrn.com/abstract=2965135 or http://dx.doi.org/10.2139/ssrn.2965135

Itai Ashlagi

Stanford University - Management Science & Engineering ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

Mark Braverman

Princeton University ( email )

22 Chambers Street
Princeton, NJ 08544-0708
United States

Yash Kanoria

Columbia Business School - Decision Risk and Operations ( email )

New York, NY
United States

Peng Shi (Contact Author)

University of Southern California - Marshall School of Business ( email )

701 Exposition Blvd
Los Angeles, CA 90089
United States

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