Motivated by markets for ''expertise,'' we study a bandit model where a principal chooses between a safe and risky arm. A strategic agent controls the risky arm and privately knows whether its type is high or low. Irrespective of type, the agent wants to maximize duration of experimentation with...

We study the design of contracts that incentivize experts to collect information and truthfully report it to a decision maker. We depart from most of the previous literature by assuming that the transfers cannot depend on the realized state or on the ex post payoff of the decision maker. The...

A decision maker, named Alice, wants to know if an expert has significant information about payoff-relevant probabilities of future events. The expert, named Bob, either knows this probability almost perfectly or knows nothing about it. Hence, both Alice and the uninformed expert face...

A decision maker needs predictions about the realization of a repeated experiment in each period. An expert provides a theory that, conditional on each finite history of outcomes, supplies a probabilistic prediction about the next outcome. However, there may be false experts without any...

An agent searches sequentially for advice from multiple experts concerning the payoff of taking an operation. After incurring a positive search cost, the agent can consult an expert, whose interest is partially aligned with him. There are infinitely many experts, each has access to an...

We introduce a learning framework in which a principal seeks to determine the ability of a strategic agent. The principal assigns a test consisting of a finite sequence of tasks. The test is adaptive: each task that is assigned can depend on the agent's past performance. The probability of...