Consumers often face an overwhelming amount of information when deciding between products, and one of the primary policymaking tools available to improve their informativeness is the framing of this information. We introduce a general theoretical approach that characterizes when one frame is...

We use a controlled experiment to show that ability and belief calibration jointly determine the benefits of working with Artificial Intelligence (AI). AI improves performance more for people with low baseline ability. However, holding ability constant, AI assistance is more valuable for people...

The central question we address in this paper is: what can an analyst infer from choice data about what a decision maker has learned? The key constraint we impose, which is shared across models of Bayesian learning, is that any learning must be rationalizable. To implement this constraint, we...

Consumers often face an overwhelming amount of information when deciding between products, and one of the primary policymaking tools available to improve their informativeness is the framing of this information. We introduce a general theoretical approach that characterizes when one frame is...

When choices are inconsistent due to behavioral biases, there is a theoretical debate about whether the structure of a model is necessary for providing precise welfare guidance based on those choices. To address this question empirically, we use standard data sets from the lab and field to...

Consumers often face an overwhelming amount of information when deciding between products, and one of the primary policymaking tools available to improve their informativeness is the framing of this information. We introduce a general theoretical approach that characterizes when one frame is...