Title: On Dynamics-Informed, Learning-Aware Mechanism Design

Abstract: Statistical decisions are often given meaning in the context of other decisions, particularly when there are scarce resources to be shared. Managing such sharing is one of the classical goals of microeconomics, and it is given new relevance in the modern setting of large, human-focused datasets, and in data-analytic contexts such as classifiers and recommendation systems.  I'll discuss several recent projects in this setting, including leader/follower dynamics in strategic classification, the robust learning of optimal auctions, Lyapunov theory for matching markets with transfers, and the use of contract theory as a way to design mechanisms for statistical inference.