Learning Under Ambiguity

Abstract

This paper considers learning when the distinction between risk and ambiguity matters. It first describes thought experiments, dynamic variants of those provided by Ellsberg, that highlight a sense in which the Bayesian learning model is extreme - it models agents who are implausibly ambitious about what they can learn in complicated environments. The paper then provides a generalization of the Bayesian model that accommodates the intuitive choices in the thought experiments. In particular, the model allows decision-makers’ confidence about the environment to change — along with beliefs — as they learn. A calibrated portfolio choice application shows how this property induces a trend towards more stock market participation and investment.ambiguity, learning, noisy signals, ambiguous signals, quality information, portfolio choice, portfolio diversification, Ellsberg Paradox