We consider two players facing identical discrete-time bandit problems with a safe and a risky arm. In any period, the risky arm yields either a success or a failure, and the first success reveals the risky arm to dominate the safe one. When payoffs are public information, the ensuing free-rider...

This paper introduces a contest model in which each player decides when to stop a privately observed Brownian motion with drift and incurs costs depending on his stopping time. The player who stops his process at the highest value wins a prize. Applications of the model include procurement...

We consider the optimal taxation of a good which exhibits a negative externality, in a setting where agents differ in their value for the good, their disutility for the externality and their value for money, and the planner observes neither. Pigouvian taxation is the unique Pareto efficient...

We establish convergence of beliefs and actions in a class of one-dimensional learning settings in which the agent’s model is misspecified, she chooses actions endogenously, and the actions affect how she misinterprets information. The crucial assumptions of our model are that the state and...

We model the joint distribution of choice probabilities and decision times in binary decisions as the solution to a problem of optimal sequential sampling, where the agent is uncertain of the utility of each action and pays a constant cost per unit time for gathering information. We show that...

We provide a tractable model of competition in rank-order contests with general prize structures. A leading example of such a situation is the competition between fund managers whose compensation depends on how well they performed relative to their peers. We analyze their trading behavior In a...

We study learning and information acquisition by a Bayesian agent who is misspecified in the sense that his prior belief assigns probability zero to the true state of the world. In our model, at each instant the agent takes an action and observes the corresponding payoff, which is the sum of the...

We explore the learning process and behavior of an individual with unrealistically high expectations (“overconfidence”) when outcomes also depend on an external fundamental that affects the optimal action. Moving beyond existing results in the literature, we show that the agent's beliefs...

We analyze dynamic allocations in a model with uncertain demand and with unobservable arrivals. The planner learns along the way about future demand, but strategic agents, who anticipate this behavior, strategically choose the timing of their arrivals. We examine the conditions under which the...

We study information design settings where the designer controls information about a state, and there are multiple agents interacting in a game who are privately informed about their types. Each agent’s utility depends on all agents’ types and actions, as well as (linearly) on the state. To...