We construct a bilateral search model of the housing market in which agents differ in their flow rewards while searching. Buyers and sellers enter the market with high flow rewards, but move at a Poisson rate to a state with low flow rewards if they do not transact in the meantime. We...

Reputation systems have emerged as important sources of information in modern economies. This paper develops a model of reputation systems that puts buyers and sellers inside a stochastic environment involving asymmetric information and search frictions, and gives them a set of options with...

We study how a continuum of agents learn about disseminated information in a dynamic beauty contest model when they do not observe aggregate variables, such as prices or quantities, but randomly observe each other's actions. We solve for the market equilibrium and find that the average learning...

In this paper we examine the risk situation facing individuals in the labor market. The current consensus in the literature is that the labor income process has a large random walk component. We argue two points. First, the direct estimates of this parameter (from labor income data) appear to be...

This paper addresses the output-price volatility puzzle by studying the interaction of optimal monetary policy and agents' beliefs. We assume that agents choose their information acquisition rate by minimizing a loss function that depends on expected forecast errors and information costs....

We consider a college admissions problem with uncertainty. We realistically assume that (i) students' college application choices are nontrivial because applications are costly, (ii) college rankings of students are noisy and thus uncertain at the time of application, and (iii) matching between...

In the canonical learning model, the multi-armed bandit with independent arms, a decision maker learns about the different alternatives only through his private experience. It is well known that any optimal experimentation strategy for this problem is ex-post inefficient: it sometimes leads the...