Data-Driven Mechanism Design: Jointly Eliciting Preferences and Information

Understand

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.