Optimally Deceiving a Learning Leader in Stackelberg Games

17^th June 2020, 13:00
Francisco Mamolego
University of Oxford

Abstract

In this talk, I will present some recent results on optimal deception in Stackelberg games. Recent work in the ML community has revealed that learning algorithms used to compute the optimal strategy for the leader to commit to in a Stackelberg game, are susceptible to manipulation by the follower. Such a learning algorithm operates by querying the best responses or the payoffs of the follower, who consequently can deceive the algorithm by responding as if his payoffs were much different than what they actually are. For this strategic behavior to be successful, the main challenge faced by the follower is to pinpoint the payoffs that would make the learning algorithm compute a commitment so that best responding to it maximizes the follower's utility, according to his true payoffs. While this problem has been considered before, the related literature has only focused on the simplified scenario in which the follower payoff space is finite, thus leaving the general version of the problem unanswered. Our results fill in this gap, by showing that it is always possible for the follower to compute (near-)optimal fake payoffs in various learning scenarios between the leader and follower.

Joint work with G. Birmpas, J. Gan, A. Hollender, N. Rajgopal and A. Voudouris