Thickness and Information in Dynamic Matching Markets

In this Department of Economics Theory Seminar, Mohammad Akbarpour from Stanford University discusses his paper on Thickness and Information in Dynamic Matching Markets.

Mohammad introduces 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.

He shows 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. The planner’s decision problem in our model involves a combinatorially complex state space.

He argues 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, a setting where agents have private information about their departure times, and design a continuous-time dynamic mechanism to elicit this information is considered.