By Shuo Han (University of Illinois at Chicago)

Talk Abstract:In incentive design, a decision-maker (called the leader) aims to induce desired behaviors in one or more agents (called the followers) by influencing the payoff of the followers. In this talk, I will focus on the setting of a non-myopic follower, who makes sequential decisions and plans by maximizing the cumulative reward, and a leader who can modify the reward of the follower. While algorithms exist for solving the incentive design problem, they rely on several restrictive assumptions about the follower: 1) When the best response is non-unique, the follower breaks ties in favor of the leader; 2) the leader knows perfectly how the modified reward is perceived by the follower; 3) the follower is fully rational. Motivated by the need for removing these assumptions, we propose to study the problem of robust incentive design, where the goal is to obtain a robust strategy for the leader to achieve nearly optimal performance when these assumptions do not hold. I will show that such a robust strategy can be numerically computed using mixed-integer linear programming. In addition, I will also discuss conditions under which a robust strategy is guaranteed to exist.

Speaker Bio:Shuo Han is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois Chicago (UIC). Before joining UIC, he was a postdoctoral researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He received his B.E. and M.E. in Electronic Engineering from Tsinghua University, and his Ph.D. in Electrical Engineering from the California Institute of Technology. His research interests lie broadly in the areas of optimization and control theory, with a particular emphasis on multi-agent systems.