We develop an equilibrium framework that relaxes the standard assumption that people
have a correctly specified view of their environment. Each player is characterized by a …

Motivated by their increasing prevalence, we study outcomes when competing sellers use
machine learning algorithms to run real-time dynamic price experiments. These algorithms …

What do we notice and how does this affect what we learn and come to believe? I present a
model of an agent who learns to make forecasts on the basis of readily available …

We show that search frictions in credit markets affect accepted interest rates and loan sizes
and distort consumption. Using data on car loan applications and originations not …

Different agents need to make a prediction. They observe identical data, but have different
models: they predict using different explanatory variables. We study which agent believes …

We consider an agent who represents uncertainty about the environment via a possibly
misspecified model. Each period, the agent takes an action, observes a consequence, and …

I study individuals who use frequentist models to draw uniform inferences from independent
and identically distributed data. The main contribution of this paper is to show that distinct …

Computer systems are increasingly heterogeneous with nodes consisting of CPUs and GPU
accelerators. As such systems become mainstream, they move away from specialized high …

Comparing self-reports to administrative data records on diagnosis and prescription drug
use, we find that survey respondents under-report mental health conditions 36% of the time …

This paper studies categorizations that are optimal for the purpose of making predictions. A
subject encounters an object (x, y). She observes the first component, x, and has to predict …