Asymptotically Optimal Control of a Centralized Dynamic Matching Market with General Utilities

Jose H. Blanchet (), Martin I. Reiman (), Virag Shah (), Lawrence M. Wein () and Linjia Wu ()
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Jose H. Blanchet: Management Science and Engineering Department, Stanford University, Stanford, California 94305
Martin I. Reiman: Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027
Virag Shah: Management Science and Engineering Department, Stanford University, Stanford, California 94305
Lawrence M. Wein: Graduate School of Business, Stanford University, Stanford, California 94305
Linjia Wu: Management Science and Engineering Department, Stanford University, Stanford, California 94305

Operations Research, 2022, vol. 70, issue 6, 3355-3370

Abstract: We consider a matching market where buyers and sellers arrive according to independent Poisson processes at the same rate and independently abandon the market if not matched after an exponential amount of time with the same mean. In this centralized market, the utility for the system manager from matching any buyer and any seller is a general random variable. We consider a sequence of systems indexed by n where the arrivals in the n th system are sped up by a factor of n . We analyze two families of one-parameter policies: the population threshold policy immediately matches an arriving agent to its best available mate only if the number of mates in the system is above a threshold, and the utility threshold policy matches an arriving agent to its best available mate only if the corresponding utility is above a threshold. Using an asymptotic fluid analysis of the two-dimensional Markov process of buyers and sellers, we show that when the matching utility distribution is light-tailed, the population threshold policy with threshold n ln n is asymptotically optimal among all policies that make matches only at agent arrival epochs. In the heavy-tailed case, we characterize the optimal threshold level for both policies. We also study the utility threshold policy in an unbalanced matching market with heavy-tailed matching utilities and find that the buyers and sellers have the same asymptotically optimal utility threshold. To illustrate our theoretical results, we use extreme value theory to derive optimal thresholds when the matching utility distribution is exponential, uniform, Pareto, and correlated Pareto. In general, we find that as the right tail of the matching utility distribution gets heavier, the threshold level of each policy (and hence market thickness) increases, as does the magnitude by which the utility threshold policy outperforms the population threshold policy.

Keywords: Stochastic Models; matching markets; queueing asymptotics; regularly varying functions; extreme value theory (search for similar items in EconPapers)
Date: 2022
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