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 2418R2, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: We study mechanism design in environments where agents have private preferences and private information about a common payoff-relevant state. In such settings with multi-dimensional types, standard mechanisms fail to implement efficient allocations. We address this limitation by proposing data-driven mechanisms that condition transfers on additional post-allocation information, modeled as an estimator of the payoff-relevant state. Our mechanisms extend the classic Vickrey-Clarke-Groves framework. We show they achieve exact implementation in posterior equilibrium when the state is fully revealed or utilities are affine in an unbiased estimator. With a consistent estimator, they achieve approximate implementation that converges to exact implementation as the estimator converges, and we provide bounds on the convergence rate. We demonstrate applications to digital advertising auctions and AI shopping assistants, where user engagement naturally reveals relevant information, and to procurement auctions with consumer spot markets, where additional information arises from a pricing game played by the same agents.

Pages: 54 pages
Date: 2025-12-23
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