Dynamic Matching: Characterizing and Achieving Constant Regret

Süleyman Kerimov (), Itai Ashlagi () and Itai Gurvich ()
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Süleyman Kerimov: Jones Graduate School of Business, Rice University, Houston, Texas 77005
Itai Ashlagi: Department of Management Science and Engineering, Stanford University, Stanford, California 94305
Itai Gurvich: Kellogg School of Management, Northwestern University, Evanston, Illinois 60208

Management Science, 2024, vol. 70, issue 5, 2799-2822

Abstract: We study how to optimally match agents in a dynamic matching market with heterogeneous match cardinalities and values. A network topology determines the feasible matches in the market. In general, a fundamental tradeoff exists between short-term value—which calls for performing matches frequently—and long-term value—which calls, sometimes, for delaying match decisions in order to perform better matches. We find that in networks that satisfy a general position condition, the tension between short- and long-term value is limited, and a simple periodic clearing policy (nearly) maximizes the total match value simultaneously at all times. Central to our results is the general position gap ϵ ; a proxy for capacity slack in the market. With the exception of trivial cases, no policy can achieve an all-time regret that is smaller, in terms of order, than ϵ − 1 . We achieve this lower bound with a policy, which periodically resolves a natural matching integer linear program, provided that the delay between resolving periods is of the order of ϵ − 1 . Examples illustrate the necessity of some delay to alleviate the tension between short- and long-term value.

Keywords: dynamic matching; queueing; optimal control (search for similar items in EconPapers)
Date: 2024
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