Data-Driven Mechanism Design: Jointly Eliciting Preferences and Information

Dirk Bergemann, Marek Bojko, Paul DŸtting, Renato Paes Leme, Haifeng Xu and Song Zuo
Additional contact information
Dirk Bergemann: Yale University
Marek Bojko: Yale University
Paul DŸtting: Google Research
Renato Paes Leme: Google Research
Haifeng Xu: University of Chicago and Google Research
Song Zuo: Google Research

No 2418R1, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: We study mechanism design when agents have private preferences and private information about a common payoff-relevant state. We show that standard message-driven mechanisms cannot implement socially efficient allocations when agents have multidimensional types, even under favorable conditions. To overcome this limitation, we propose data-driven mechanisms that leverage additional post-allocation information, modeled as an estimator of the payoff-relevant state. Our data-driven mechanisms extend the classic Vickrey-Clarke-Groves class. We show that they achieve exact implementation in posterior equilibrium when the state is either fully revealed or the utility is affine in an unbiased estimator. We also show that they achieve approximate implementation with a consistent estimator, converging to exact implementation as the estimator converges, and present bounds on the convergence rate. We demonstrate applications to digital advertising auctions and large language model (LLM)-based mechanisms, where user engagement naturally' reveals relevant information.

Pages: 45 pages
Date: 2025-03-17
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