Current students


Section: Computer Science and Engineering

Major Research topic:
Reducing the Gap between Theory and Applications in Algorithmic Bayesian Persuasion

My work focuses on the following question: is it possible to influence the behavior of self-interested agents through the strategic provision of information? This 'sweet talk' is ubiquitous among all sorts of economics and non-economics activities. In this work, we model these multi-agent systems as games between an informed sender and one or multiple receivers. We study the computational problem faced by an informed sender that wants to use his information advantage to influence rational receivers with the partial disclosure of information. In particular, the sender faces an information structure design problem that amounts to deciding 'who gets to know what'.
Bayesian persuasion provides a formal framework to model these settings as asymmetric-information games. In recent years, much attention has been given to Bayesian persuasion in the economics and artificial intelligence communities due also to the applicability of this framework to a large class of scenarios like online advertising, voting, traffic routing, recommendation systems, security, and product marketing. However, there is still a large gap between the theoretical study of information in games and its applications in real-world scenarios. This work contributes to close this gap along two directions. First, we study the persuasion problem in real-world scenarios, focusing on voting, routing, and auctions. Then, we relax stringent assumptions that limit the applicability of the Bayesian persuasion framework in practice. In particular, the classical model assumes that the sender has perfect knowledge of the receiver's utility. We remove this assumption initiating the study of an online version of the persuasion problem. This is the first step in designing adaptive information disclosure policies that deal with the uncertainty intrinsic in all real-world applications.