This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom.

ABSTRACT

This talk introduces dynamic game theory as a natural modeling tool for multi-agent interactions ranging from large, abstract systems such as ride-hailing networks to more concrete, physically-embodied robotic settings such as collision-avoidance in traffic. We present the key theoretical underpinnings of dynamic game models for these varied situations and draw attention to the subtleties of information structure, i.e., what information is implicitly made available to each agent in a game. Thus equipped, the talk presents a state-of-the-art technique for solving several variants of these games, as well as a set of “dual” techniques for the inverse problem of identifying players’ objectives and other structures based on observations of strategic behavior.