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Calibrated Mechanism Design

Author

Listed:
  • Laura Doval

    (Columbia Business School - Columbia University [New York], CEPR - Center for Economic Policy Research)

  • Alex Smolin

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

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.

Suggested Citation

  • Laura Doval & Alex Smolin, 2026. "Calibrated Mechanism Design," Working Papers hal-05520485, HAL.
    • Handle: RePEc:hal:wpaper:hal-05520485
    DOI: 10.48550/arXiv.2512.17858
    Note: View the original document on HAL open archive server: https://hal.science/hal-05520485v1
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