Sequential Learning under Informational Ambiguity

Abstract

This paper studies a sequential learning problem where individuals are ambiguous about other people's data-generating processes. This paper finds that the occurrence of an information cascade can be interpreted as a result of ambiguity instead of details of the true data-generating process as suggested by the literature. When there is sufficient ambiguity, for all possible data-generating processes, an information cascade occurs almost surely. This paper further shows that non-cascade may even represent a knife-edge case with respect to ambiguity. An arbitrarily small degree of ambiguity can produce a cascade when signals are bounded and destroy complete learning when signals are unbounded.

Keywords: Social learning, information cascades, ambiguity, herding

JEL Classification: D81, D83, C72