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Unrealistic Expectations and Misguided Learning

Econometrica 2018 86(4), 1159-1214
We explore the learning process and behavior of an individual with unrealistically high expectations (overconfidence) when outcomes also depend on an external fundamental that affects the optimal action. Moving beyond existing results in the literature, we show that the agent's beliefs regarding the fundamental converge under weak conditions. Furthermore, we identify a broad class of situations in which “learning” about the fundamental is self‐defeating: it leads the individual systematically away from the correct belief and toward lower performance. Due to his overconfidence, the agent—even if initially correct—becomes too pessimistic about the fundamental. As he adjusts his behavior in response, he lowers outcomes and hence becomes even more pessimistic about the fundamental, perpetuating the misdirected learning. The greater is the loss from choosing a suboptimal action, the further the agent's action ends up from optimal. We partially characterize environments in which self‐defeating learning occurs, and show that the decisionmaker learns to take the optimal action if, and in a sense only if, a specific non‐identifiability condition is satisfied. In contrast to an overconfident agent, an underconfident agent's misdirected learning is self‐limiting and therefore not very harmful. We argue that the decision situations in question are common in economic settings, including delegation, organizational, effort, and public‐policy choices.

Speed, Accuracy, and the Optimal Timing of Choices

American Economic Review 2018 108(12), 3651-3684
We model the joint distribution of choice probabilities and decision times in binary decisions as the solution to a problem of optimal sequential sampling, where the agent is uncertain of the utility of each action and pays a constant cost per unit time for gathering information. We show that choices are more likely to be correct when the agent chooses to decide quickly, provided the agent’s prior beliefs are correct. This better matches the observed correlation between decision time and choice probability than does the classical drift-diffusion model (DDM), where the agent knows the utility difference between the choices. (JEL C41, D11, D12, D83)