Abstract

We study a framework for robust mechanism design that can accommodate various degrees of robustness with respect to agents’ beliefs with the belief-free and Bayesian settings as special cases. For general belief restrictions, we characterize incentive compatible direct mechanisms in general environments with interdependent values. Our main result provides a first order approach to incentive design under belief restrictions. It informs the design of transfers via belief-based terms to attain incentive compatibility. Using the resulting design principle, we provide possibility results in environments that violate standard single-crossing and monotonicity conditions. We discuss a robust version of the revenue equivalence theorem that holds under a notion of independence generalized to non-Bayesian settings. Further, to contrast implications with the well-known anything goes results from Bayesian mechanism design, within our framework, we study a fairly general model of comovement of payoff types and beliefs. We show that while, under comovement, implementation possibilities are rich, full rent extraction might not follow. 

Bio

Mariann Ollár is an Assistant Professor of Economics at NYU Shanghai. She is a researcher in economics who contributes to the fields of Game Theory and Mechanism Design. At NYU Shanghai, Mariann teaches Game Theory, Microeconomics, and Market Design. She is from Hungary and studied in Budapest and then, received her PhD from the University of Wisconsin, Madison. Her papers have been published in the American Economic Review, the Review of Economic Studies and the Economics Letters. Her research focuses on the robustness of organizations and institutions; and the role of beliefs and transfers in economic settings.

Picture: https://shanghai.nyu.edu/academics/faculty/directory/mariann-ollar