Strategic Information Disclosure to People with Multiple Alternatives

In this article, we study automated agents that are designed to encourage humans to take some actions over others by strategically disclosing key pieces of information. To this end, we utilize the framework of persuasion games-a branch of game theory that deals with asymmetric interactions where one player (Sender) possesses more information about the world, but it is only the other player (Receiver) who can take an action. In particular, we use an extended persuasion model, where the Sender's information is imperfect and the Receiver has more than two alternative actions available. We design a computational algorithm that, from the Sender's standpoint, calculates the optimal information disclosure rule. The algorithm is parameterized by the Receiver's decision model (i.e., what choice he will make based on the information disclosed by the Sender) and can be retuned accordingly. We then provide an extensive experimental study of the algorithm's performance in interactions with human Receivers. First, we consider a fully rational (in the Bayesian sense) Receiver decision model and experimentally show the efficacy of the resulting Sender's solution in a routing domain. Despite the discrepancy in the Sender's and the Receiver's utilities from each of the Receiver's choices, our Sender agent successfully persuaded human Receivers to select an option more beneficial for the agent. Dropping the Receiver's rationality assumption, we introduce a machine learning procedure that generates a more realistic human Receiver model. We then show its significant benefit to the Sender solution by repeating our routing experiment. To complete our study, we introduce a second (supply-demand) experimental domain and, by contrasting it with the routing domain, obtain general guidelines for a Sender on how to construct a Receiver model.