Abstract: There has been a surge of interest in the role the information structure of a game --- i.e. who knows what --- plays in determining the game's equilibrium and players' payoffs. Underlying much of this economic literature is an algorithmic question faced by the mechanism designer: which information structure leads to "optimal" equilibria, and can it be implemented via a simple (efficient) algorithm? This task is sometimes known as "signaling" or "persuasion". I will survey work in both economics and computer science examining structural and computational aspects of this question, highlighting some of our recent progress.
Bio: Shaddin Dughmi is an Assistant Professor in the Department of Computer Science at USC, where he is a member of the Theory Group. He received a B.S. in computer science, summa cum laude, from Cornell University in 2004, and a PhD in computer science from Stanford University in 2011. He is a recipient of the NSF CAREER award, the Arthur L. Samuel best doctoral thesis award, and the ACM EC best student paper award.