This paper considers learning when the distinction between risk and ambiguity (Knightian uncertainty) matters. Working within the framework of recursive multiple-priors utility, the paper formulates a counterpart of the Bayesian model of learning about an uncertain parameter from conditionally...

The inability of the Bayesian model to accomodate Ellsberg-type behavior is well known. This paper focuses on another limitation of the Bayesian model, specific to a dynamic setting, namely the inability to permit a distinction between experiments that are identical and those that are only...

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...