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Hi! I’m a Ph.D. Candidate in Economics at Cornell University, with an interest in games, information, and applied theory. My work focuses on practical applications of information design. My committee consists of David Easley, Tommaso Denti, and Larry Blume.

Research

I consider a model of Bayesian persuasion with observational learning. A sender designs an experiment to encourage adoption of a new product, which may be either good or bad. The experiment and its outcome
are viewed by an opinion leader, who chooses whether or not to adopt the sender’s product. If the opinion leader choose to adopt, the experiment and its outcome are next viewed by an audience who also makes an adopt/reject decision. Both opinion leader and audience strictly prefer to adopt the product when it is good, and reject it when it is bad. The cost of adoption is heterogeneous for each individual, and each individual’s adoption cost is their own private information. The sender’s goal is to maximize the probability of audience adoption. In a binary setting, I show that audience welfare can be identified with the informativeness of the equilibrium signal. I prove that the audience weakly prefers the opinion leader to be present in a variety of settings. I find that while collusion between the firm and opinion leader can hurt audience welfare, it still cannot cause the opinion leader’s presence to harm the audience. Notably, for some distributions of preferences, the audience may be made better off if the opinion leader shares her private information with the sender. I provide further analysis of audience welfare across regimes, show that these results are robust to non-common prior beliefs, and consider an extension to a more general state space.

The Bayesian persuasion framework assumes a sender can choose any random map from the state to any outcome space; I present a model which questions the importance of this assumption. A biased researcher chooses how many subjects to enroll in a trial; each subject improves with some probability when the treatment is good and complementary probability when it is bad. Under pre-registration, the researcher commits to a sample size, while under sequential sampling he observes each subject’s condition before deciding whether to continue or end the trial. I show that as the information contained in a given subject outcome vanishes, under sequential sampling the sender can obtain his first-best Bayesian persuasion equilibrium outcome, but under pre-registration preliminary results suggest the sender cannot do much better than full revelation.

“Hear-No-Evil” Equilibrium (Work in Progress)

A Bayes correlated equilibrium of an incomplete information game (G,S) is any outcome that an omniscient mediator could induce with an appropriate information design policy. Bergemann and Morris (2016) show that the mediator can restrict attention to messages which are incentive-compatible action recommendations, and also that set of BCE is shrinking in the baseline level of information S.  However, even though the mediator’s message may be incentive-compatible for an agent to follow after hearing it, it is possible that the agent would prefer not to hear it in the first place. In this paper I define a refinement of Bayes correlated equilibrium which I refer to as hear-no-evil Bayes correlated equilibrium, in which the mediator’s messages must also be incentive-compatible for agents to hear; I show that the set of HNEBCE of (G,S) is shrinking in S if and only if this hear-no-evil condition has no bite.

CV