Learning is crucial to organizational decision making but often needs to be delegated. We examine a dynamic delegation problem where a principal decides on a project with uncertain profitability. A biased agent, who is initially as uninformed as the principal, privately learns the profitability...

This paper shows that belief‐driven economic fluctuations are a general feature of many determinate macroeconomic models. In environments with hidden state variables, forecast‐model misspecification can break the link between indeterminacy and sunspots by establishing the existence of...

We solve a long-term contracting problem with symmetric uncertainty about the agent's quality, and a hidden action of the agent. As information about quality accumulates, incentives become easier to provide because the agent has less room to manipulate the principal's beliefs. This result is...

We develop a model of information exchange through communication and investigate its implications for information aggregation in large societies. An \textit{underlying state} determines payoffs from different actions. Agents decide which others to form a costly \textit{communication link} with,...

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...

This paper examines the interplay between career concerns and market structure. Ability and effort are complements: effort increases the probability that a skilled agent achieves a one-time breakthrough. Wages are based on assessed ability and on expected output. Effort levels at different times...

The main purpose of this paper is to provide a simple criterion enabling to conclude that two agents do not share a common prior. The criterion is simple, as it does not require information about the agents' knowledge and beliefs, but rather only the record of a dialogue between the agents. In...

While payoff-based learning models are almost exclusively devised for finite action games, where players can test every action, it is harder to design such learning processes for continuous games. We construct a stochastic learning rule, designed for games with continuous action sets, which...

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. Our stochastic-approximation-based methods rely on two crucial...

We analyze boundedly rational learning in social networks within binary action environments. We establish how learning outcomes depend on the environment (i.e., informational structure, utility function), the axioms imposed on the updating behavior, and the network structure. In particular, we...