Linde Institute/SISL Seminar: Itai Ashlagi, Stanford University
Clearing matching markets efficiently: informative signals and match recommendations
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 about their preferences agents need to learn and communicate with others to form the final matching. Previous results by Segal (2007) and Gonczarowski et al. (2015) suggest that a high level of congestion is inevitable under arbitrary preferences, in order to find a market clearing outcome (stable matching). We show that when the unobservable component of agent preferences satisfy certain natural assumptions, it is possible to recommend potential matches and encourage informative signals such that 1) the market reaches an equilibrium outcome, 2) the overall communication overhead is small and 3) 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 whom the agent has a non-negligible chance of both liking and being liked by, as estimated by the observable component of preferences and prior expression of interest by agents on the other side based on the unobservable component of their preferences.
Based on joint work with Mark Braverman, Yash Kanoria, and Peng Shi.
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