TEC (Theory) Seminar: Laura Doval, Columbia

Speaker

Laura Doval, Columbia

Biography

I am the Chong Khoon Lin Professor of Business in the Economics Division at Columbia Business School. I am a microeconomic theorist working in the areas of game theory, mechanism design, and market design.

I am a Research Affiliate at the Centre for Economic Policy Research (CEPR) (Organizational Economics). I currently serve as an Associate Editor at Theoretical Economics and the Journal of the European Economic Association, and as Coeditor at Economic Theory.

I received my Ph.D. from Northwestern in 2016. In 2021, I was elected as a Fellow of the Society for the Advancement of Economic Theory. In 2024, I received a Sloan Fellowship.

Title

"Calibrated Mechanism Design"

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 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 algorithm to characterize calibrated mechanisms, combining information design and mechanism design. When agents’ payoffs are state-independent, 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 compatibility to robustness against undetectable deviations—a distinction that vanishes in transferable utility settings.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 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 algorithm to characterize calibrated mechanisms, combining information design and mechanism design. When agents’ payoffs are state-independent, 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 compatibility to robustness against undetectable deviations—a distinction that vanishes in transferable utility settings.