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...

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 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...