Computational Aspects of Optimal Information Revelation

What is the best way to reveal information to other strategic players? This is a question we want to solve during a poker game at home, as well as in billion-dollar industries like online ad auctions. The strategic decisions of the players depend crucially on the information available to them, and the act of exploiting an informational advantage to influence the behavior of others is indeed universal. In Bayesian games, players’ payoffs often depend on the state of nature, which may be hidden from the players. Instead, players receive a signal regarding the state of nature which they use to form beliefs about their payoffs, and choose their strategies. Thus the strategic decisions and payoffs of the players depend crucially on the information available from the signal they receive. In this thesis, we study the optimization problem faced by an informed principal, who must choose how to reveal information in order to induce a desirable equilibrium, a task often referred to as information structure design, signaling or persuasion. Similar to classic mechanism design 1, we have a principal who is interested in the outcome of the game, but the difference is that the principal influences