Signaling in Bayesian Stackelberg Games

Algorithms for solving Stackelberg games are used in an ever-growing variety of real-world domains. Previous work has extended this framework to allow the leader to commit not only to a distribution over actions, but also to a scheme for stochastically signaling information about these actions to the follower. This can result in higher utility for the leader. In this paper, we extend this methodology to Bayesian games, in which either the leader or the follower has payoff-relevant private information or both. This leads to novel variants of the model, for example by imposing an incentive compatibility constraint for each type to listen to the signal intended for it. We show that, in contrast to previous hardness results for the case without signaling, we can solve unrestricted games in time polynomial in their natural representation. For security games, we obtain hardness results as well as efficient algorithms, depending on the settings. We show the benefits of our approach in experimental evaluations of our algorithms.

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