Jaden Chen, Cornell University-Ph.D. Candidate

Sequential Learning under Informational Ambiguity

Abstract: This paper studies a sequential learning problem where individuals are ambiguous about other people's signal structures. It finds that ambiguity has an important impact on social learning and provides new insights on the mechanism behind herding behavior. This paper claims that whether an information cascade occurs is a result of individuals' ambiguity level instead of specific statistical features of the actual signal processes as suggested by previous literature. When there is sufficient ambiguity, for all possible data-generating processes, an information cascade occurs almost surely. Moreover, a slight degree of ambiguity suffices to produce a cascade when signals are bounded and destroys full learning when signals are unbounded. As an extension, this paper also investigates the case where there is an outside option. It finds that an information cascade occurs on this outside option when there is sufficient ambiguity and individuals are ambiguity-averse.

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