Learning a Stackelberg Leader’s Incentives from Optimal Commitments

Speaker: Dr. Yurong Chen Date: 23-09-2025 2pm-3pm (BST) Location: Department of Computer Science CS1.04, University of Warwick, Coventry, UK

Abstract

Stackelberg equilibria, as functions of the players’ payoffs, can inversely reveal information about the players’ incentives. In this paper, we study to what extent one can learn about the leader’s incentives by actively querying the leader’s optimal commitments against strategically designed followers. We show that, by using polynomially many queries and operations, one can learn a payoff function that is strategically equivalent to the leader’s, in the sense that: 1) it preserves the leader’s preference over almost all strategy profiles; and 2) it preserves the set of all possible (strong) Stackelberg equilibria the leader may engage in, considering all possible follower types. As an application, we show that the information acquired by our algorithm is sufficient for a follower to induce the best possible Stackelberg equilibrium by imitating a different follower type. To the best of our knowledge, we are the first to demonstrate that this is possible without knowing the leader’s payoffs beforehand. Due caution is necessary when one intends to utilize the power of optimal commitment. This is a joint work with Xiaotie Deng (Peking University), Jiarui Gan (University of Oxford), and Yuhao Li (Columbia University).

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About Dr. Yurong Chen

Yurong Chen is currently a postdoc at the SIERRA-team, INRIA Paris, working with Michael I. Jordan. She earned her PhD in Computer Science at Peking University, where she was advised by Xiaotie Deng, and holds a bachelor’s degree in Applied Mathematics from the Hua Luogeng Honors Class at Beihang University. Her research focuses on the intersection of learning and game theory, especially on how strategic agents exploit information advantage against learning agents. She is a recipient of the Best Student Paper Award at WINE 2022.