We propose an approach to study informationally robust dynamic pricing when the seller lacks commitment.
We conduct an experiment on belief updating and document that subjects under-react to information when it is in the form of a retraction.
We exhibit an algorithm which ensures that agents play a Bayesian best reply (approximately).
We introduce a selection criterion on behavioral biases in environments with learning, and show that it need not select for a bias-free worldview in some common applications.
A monopolist can ensure an optimal price is used in the long run by designing an algorithm that assumes a linear demand curve, even if demand is quite nonlinear.
We empirically study registries, focusing mostly (but not exclusively) on the AEA RCT Registry, and theoretically discuss issues related to incentives behind registration. The Mathematica notebook referenced in the Appendix can be found here.
I demonstrate how to recover a decisionmaker's information structure from posterior beliefs over states, together with posterior beliefs that each signal could be observed. In the process, I make new observations on the geometric structure of information.
We propose a classification algorithm with generalizes AdaBoost to a multi-class, which requires an intuitive and simple to check condition to be valid.
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