We model fairness within the context of search. To do this, consider sequential search where the searcher is uncertain about the underlying pool of candidates. Fairness is introduced by requiring that candidates cannot be discriminated against by non-merit characteristics or by the order in...

We compare information design, or Bayesian persuasion, by uninformed and informed principals. We show that, under the assumptions of monotone preferences of the principal and nondegenerate information structures, a Pareto undominated outcome is implementable by the uninformed principal if and...

We compare information design, or Bayesian persuasion, by uninformed and informed principals. We show that, under the assumptions of monotone preferences of the principal and nondegenerate information structures, a Pareto undominated outcome is implementable by the uninformed principal if and...

This paper provides a framework to study communication conflicts, such as political debates, using a novel model of competition in Bayesian persuasion. Debating parties can "frame" their arguments for maximal impact. They also can "spam" the discussion to distract the audience from the...

We introduce a solution concept for extensive-form games of incomplete information in which players are allowed to entertain multiple beliefs about what they do not know in the game. Players make dynamically consistent choices by looking for compromises that yield a good performance under each...

We study an information design problem where two competing senders move sequentially. The second mover can either reveal more information (constructive argumentation) or obfuscate the first mover's information (toxic argumentation). We show that sequential obfuscation of an initially disclosed...

Two debaters address an audience by sequentially choosing their information strategies. We compare the setting where the second mover reveals additional information (constructive argumentation) with the setting where the second mover obfuscates the first mover's information (toxic...

We consider an agent who has to repeatedly make choices in an uncertain and changing environment, who has full information of the past, who discounts future payoffs, but who has no prior. We provide a learning algorithm that performs almost as well as the best of a given finite number of experts...