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 develop a nonparametric procedure, called the lattice method, for testing the consistency of contingent consumption data with a broad class of models of choice under risk and under uncertainty. Our method allows for risk loving and elation seeking behavior and can be used to calculate, via...

Consider a finite data set where each observation consists of a bundle of contingent consumption chosen by an agent from a constraint set of such bundles. We develop a general procedure for testing the consistency of this data set with a broad class of models of choice under risk and under...