DREAM Seminar by Laura Doval (Columbia)

'The Core of Bayesian Persuasion', joint with Ran Eilat

Abstract
An analyst observes the frequency with which an agent takes actions, but not the frequency with which she takes actions conditional on a payoff relevant state. In this setting, we ask when the analyst can rationalize the agent’s choices as the outcome of the agent learning something about the state before taking action. Our characterization marries information design (Bergemann and Morris, 2016) and Bayesian persuasion (Kamenica and Gentzkow, 2011) relying on Hall’s marriage theorem and a network flows theorem (Gale, 1957). We apply our result to identify conditions under which a data set is consistent with a public information structure in a multi-agent setting, where players’ payoffs depend on their own actions and the state of the world.