UNET Seminar: Mariann Ollar (NYU Shanghai)

Title:  'Incentive Compatibility and Belief Restrictions' (with Antonio Penta)

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 derive incentive compatible direct transfer mechanisms in general environments with interdependent values. Our main result provides a first order approach to incentive design under belief restrictions. This 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, contrary to implications with the well-known anything goes results from Bayesian mechanism design, within our framework, we show under a fairly general model of comovement that, while implementation possibilities can be rich, full rent extraction typically can not be attained.