Thickness and Information in Dynamic Matching Markets
Stanford Graduate School of Business
Stanford University - Department of Economics
Shayan Oveis Gharan
University of California, Berkeley
We introduce a simple model of dynamic matching in networked markets, where agents arrive and depart stochastically, and the composition of the trade network depends endogenously on the matching algorithm. We show that if the planner can identify agents who are about to depart, then waiting to thicken the market is highly valuable, and if the planner cannot identify such agents, then matching agents greedily is close to optimal. We characterize the optimal waiting time (in a restricted class of mechanisms) as a function of waiting costs and network sparsity. The planner's decision problem in our model involves a combinatorially complex state space. However, we show that simple local algorithms that choose the right time to match agents, but do not exploit the global network structure, can perform close to complex optimal algorithms. Finally, we consider a setting where agents have private information about their departure times, and design a continuous-time dynamic mechanism to elicit this information.
Number of Pages in PDF File: 76
Keywords: Market Design, Matching, Networks, Continuous-time Markov Chains, Mechanism Design
JEL Classification: D47, C78, C60
Date posted: February 15, 2014 ; Last revised: January 30, 2016
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