In the HIDA Lecture@HDS-LEE outstanding international Data Scientists are invited to speak about their current research.
Michael I. Jordan from University of California, Berkeley will talk about "On 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 that aim to explore the interface between machine learning and microeconomics, including leader/follower dynamics in strategic classification, a Lyapunov theory for matching markets with transfers, and the use of contract theory as a way to design mechanisms that perform statistical inference.