Knowledge-Based Mechanisms
Mr Yangfan Zhou
Ph.D. Candidate in Economics
Columbia University
We study robust mechanisms when the designer possesses a Bayesian belief over some components of agents’ private information but faces ambiguity over others. The designer evaluates mechanisms by their worst-case performance over all joint distributions consistent with her belief over the Bayesian components. The frame- work encompasses settings such as multidimensional delegation in which a prin- cipal knows the distribution of the state but not the agent’s preferences (e.g., his trade-offs across dimensions), screening in which a seller only knows certain quan- tiles of the buyer’s value distribution or only has misspecified estimates of buyer preferences, and auction design or social choice when agents’ beliefs about each other are ambiguous to the designer. We provide sufficient conditions under which a knowledge-based mechanism—one that conditions only on the Bayesian compo- nents but not the ambiguous ones—is robustly optimal. Our results unify earlier work across distinct economic environments and uncover new applications.
