We study learning and information acquisition by a Bayesian agent whose prior belief is misspecified in the sense that it assigns probability 0 to the true state of the world. At each instant, the agent takes an action and observes the corresponding payoff, which is the sum of a fixed but...

We conducted controlled laboratory experiments to investigate how humans adapt the decision rule (DR) they use in repeated strategic interactions in light of new information becoming available. We do so by providing -- along different paths -- more and more information over time, so as to...

The paper analyzes a dynamic model of rational strategic learning in a network. It complements existing literature by providing a detailed picture of short-run dynamics in a game of strategic experimentation where agents are located in a social network. We show that the delay in information...

The paper analyzes a dynamic model of rational strategic learning in a network. It complements existing literature by providing a detailed picture of short-run dynamics in a game of strategic experimentation where agents are located in a social network. The delay in information transmission...

We choose between alternatives without being fully informed about the rewards from different courses of action. In making our decisions, we use our own past experience and the experience of others. So the ways in which we interact - our social network - can influence our choices. These choices...

Motivated by the idea that lack of experience is a source of errors but that experience should reduce them, we model agents' behavior using a stochastic choice model, leaving endogenous the accuracy of their choice. In some games, increased accuracy is conducive to unstable best-response...

We study learning by privately informed forward-looking agents in a simple repeated-action setting of social learning. Under a symmetric signal structure, forward-looking agents behave myopically for any degrees of patience. Myopic equilibrium is unique in the class of symmetric threshold...

This paper explores the extent to which people learn in repeated games without feedback, and the extent to which this learning transfers to new games. Current theories of learning model learning as adjustment in behavior in response to feedback about outcomes and payoffs and largely ignore the...

Agents in a network want to learn the true state of the world from their own signals and their neighbors' reports. Agents know only their local networks, consisting of their neighbors and the links among them. Every agent is Bayesian with the (possibly misspecified) prior belief that her local...

In a model of project design, evaluation, and selection, we explore how the incentives to improve the design of projects depend on the availability of funding and the process of evaluation. We show that project designers (researchers or NGOs) prefer to subject their projects to less-rigorous...