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...

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 axiomatizes an intertemporal version of multiple-ptiors utility. A central axiom is dynamic consistency, which leads to a recursive structure for utility, to 'rectangular' sets of priors and to prior-by-prior Bayesian updating as the updating rule for such sets of priors. It is argued...

When ambiguity averse investors process news of uncertain quality, they act as if they take a worst-case assessment of quality. As a result, they react more strongly to bad news than to good news. They also dislike assets for which information quality is poor, especially when the underlying...

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...

When ambiguity averse investors process news of uncertain quality, they act as if they take a worst-case assessment of quality. As a result, they react more strongly to bad news than to good news. They also dislike assets for which information quality is poor, especially when the underlying...