Close Cousins? Bayesian versus Adaptive Learning Under Model Uncertainty About Tail Risk

Abstract

On the aftermath of the global financial crisis, model uncertainty about tail risk stood out as a major problem to overcome. I capture this problem in a decision-making task in which the Bayesian agent learns whether reward prospects are fat-tailed. In simulations of the task, following adaptive learning instead markedly impairs economic performance. When asked to perform the task for real money, lab participants learned in a Bayesian way. These findings suggest that an important requirement to cope with tail risk is to learn about it optimally, and that such learning is within the reach of the average investor’s intelligence.