Research

Learning with Misspecified Models: The Case of Overestimation (JMP) (draft)(slides)

I design a framework and a laboratory experiment that allow for the comparison of multiple theories of misspecified learning. I focus on a framework with endogenous information and a data-generating process ruled by two fundamentals: an ego-relevant parameter and a state. Within this framework, I study three forces that can lead to incorrect beliefs: initial misspecifications, learning traps, and biased updating. I find that biased updating is the main driver of misspecifications in the lab. In addition, I vary the degree of ego-relevance of the parameter by introducing a stereotype treatment. The data is consistent with biased updating in both cases but for different reasons: when learning about themselves, subjects attribute successes to their own ability and failures to luck. Instead, in the stereotype treatment, they compensate for initial negative biases by over-attributing positive signals to the ability of others. This translates into similar observed choices but different dynamics in beliefs.