Abstract

http://ssrn.com/abstract=2394319
 


 



Thickness and Information in Dynamic Matching Markets


Mohammad Akbarpour


Stanford Graduate School of Business

Shengwu Li


Stanford University - Department of Economics

Shayan Oveis Gharan


University of California, Berkeley

January 2016


Abstract:     
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


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Date posted: February 15, 2014 ; Last revised: January 30, 2016

Suggested Citation

Akbarpour, Mohammad and Li, Shengwu and Oveis Gharan, Shayan, Thickness and Information in Dynamic Matching Markets (January 2016). Available at SSRN: http://ssrn.com/abstract=2394319 or http://dx.doi.org/10.2139/ssrn.2394319

Contact Information

Mohammad Akbarpour (Contact Author)
Stanford Graduate School of Business ( email )
Shengwu Li
Stanford University - Department of Economics ( email )
Landau Economics Building
579 Serra Mall
Stanford, CA 94305-6072
United States
HOME PAGE: http://www.stanford.edu/~shengwu/
Shayan Oveis Gharan
University of California, Berkeley ( email )
Berkeley, CA
United States
Feedback to SSRN


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