Calibrated Mechanism Design

TL;DR

This paper introduces a static framework called calibrated mechanism design for repeated interactions with strategic agents, characterizing implementable outcomes and showing full transparency is optimal in private values environments.

Contribution

It develops a tractable characterization of mechanisms that remain incentive compatible over repeated use, integrating information design with mechanism design principles.

Findings

Implementable outcomes correspond to two-stage mechanisms with information disclosure and fixed allocation rules.

Full transparency is optimal in private values environments, preventing surplus extraction through correlation.

Dynamic mechanisms do not expand implementable outcomes beyond weakening incentive constraints.

Abstract

We study mechanism design when a designer repeatedly uses a fixed mechanism to interact with strategic agents who learn from observing their allocations. We introduce a static framework, calibrated mechanism design, requiring mechanisms to remain incentive compatible given the information they reveal about an underlying state through repeated use. In single-agent settings, we prove implementable outcomes correspond to two-stage mechanisms: the designer discloses information about the state, then commits to a state-independent allocation rule. This yields a tractable procedure to characterize calibrated mechanisms, combining information design and mechanism design. In private values environments, full transparency is optimal and correlation-based surplus extraction fails. We provide a microfoundation by showing calibrated mechanisms characterize exactly what is implementable when an infinitely patient agent repeatedly interacts with the same mechanism. Dynamic mechanisms that condition on histories expand implementable outcomes only by weakening incentive constraints, but not by enriching the designer's ability to obfuscate learning.