Clearing Matching Markets Efficiently: Informative Signals and Match Recommendations
研究如何通过推荐潜在匹配对象和鼓励信息性信号，在满足特定偏好假设下，以低信息成本实现稳定匹配，且参与者无动机离开市场。
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. This paper was accepted by Yan Chen, decision analysis.