Upcoming Seminars

Abstract

Decentralized dynamic matching markets arise in numerous real-world applications, such as multi-hospital kidney exchange and job matching platforms. A key challenge in these markets is incentivizing self-interested agents to fully participate in a shared matching platform rather than withholding resources for their own gain. Our work studies incentive mechanisms that align individual agents' interests with socially optimal behavior in decentralized matching markets. We consider a model with multiple self-interested agents, each managing a local multi-way dynamic matching problem, where jobs of different types arrive stochastically and remain available for a limited time. Each agent holds private information about their job arrivals and actions and seeks to maximize their long-run average reward.  

We first analyze a baseline Marginal-Value (MV) mechanism, which provides incentives based on the marginal value of submitted items. While MV ensures approximate incentive alignment, we show that it fails to entirely eliminate agents’ incentives to withhold jobs due to market frictions, limiting its predictive power. To address this, we introduce two enhanced mechanisms—Marginal-Value-plus-Priority (MVP) and Marginal-Value-plus-Credit (MVC)—and use mean-field equilibrium analysis to prove that they fully eliminate the incentive to withhold jobs in finitely large markets in mean-field/oblivious equilibrium. These mechanisms introduce carefully designed state-dependent rewards. We complement our theoretical analysis with numerical experiments using date based on kidney exchange. Our simulations demonstrate that the proposed mechanisms perform well in markets with realistic sizes.

Speaker's Bio

Pengyu Qian is an Assistant Professor in the Operations & Technology Management department at the Questrom School of Business, Boston University. His research studies the design and analysis of marketplaces in dynamic settings, using tools from probability, optimization and game theory. He is interested in foundational models driven by challenges in sharing economy and the allocation of public resources. His research emphasizes algorithms/mechanisms that not only have good theoretical guarantees, but also are simple, robust, and hence practical for real-world systems. Pengyu is a recipient of the INFORMS JFIG Best Paper Prize. He earned his Ph.D. from Columbia Business School and his B.S. from Peking University.

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