Publication
1. Biased Learning under Ambiguous Information, Journal of Economic Theory [SSRN]
This paper proposes a model of how biased individuals update beliefs in the presence of informational ambiguity. Individuals are ambiguous about the actual signal-generating process and interpret signals according to the model that can best support their biases. This paper provides a complete characterization of the limit beliefs under this rule. The presence of model ambiguity has the following effects. First, it destroys correct learning even if infinitely many informative signals can be observed. When the ambiguity is sufficiently high, individuals can justify their biases, leading to belief extremism and polarization. Second, an ambiguous individual can exhibit greater confidence than a Bayesian individual with any feasible model perception. This phenomenon comes from a novel complementary effect of different models in the belief set.
This paper proposes a model of how biased individuals update beliefs in the presence of informational ambiguity. Individuals are ambiguous about the actual signal-generating process and interpret signals according to the model that can best support their biases. This paper provides a complete characterization of the limit beliefs under this rule. The presence of model ambiguity has the following effects. First, it destroys correct learning even if infinitely many informative signals can be observed. When the ambiguity is sufficiently high, individuals can justify their biases, leading to belief extremism and polarization. Second, an ambiguous individual can exhibit greater confidence than a Bayesian individual with any feasible model perception. This phenomenon comes from a novel complementary effect of different models in the belief set.
Working Paper
1. Sequential Learning under Informational Ambiguity, Revision resubmitted to Econometrica [Supplementary Material] [SSRN] new version!
This paper studies a sequential social learning problem in which 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 or its perception, as suggested by the literature. When there is sufficient ambiguity, an information cascade occurs almost surely for all possible data-generating processes. This paper further shows that standard results that feature no cascade may be fragile to arbitrarily small ambiguity.
2. Naïve Social Learning with Heterogeneous Model Perceptions [PDF]
This paper studies a social learning problem where individuals observe a sequence of signals and repeatedly communicate their beliefs with neighbors. Individuals follow a naïve rule when learning from others and may incorrectly interpret their own information. This paper provides a set of characterizations for limit beliefs in this learning problem. One key feature of the characterizations is that the society has a tendency to settle on a state that minimizes the weighted relative entropy between the true and the perceived data-generating processes, and the weight describes the network's centrality. This paper further notes that it is possible that beliefs fail to converge or converge to multiple limits, which can be characterized by a variant of the weighted relative entropy. One implication is that group irrationality can arise. The society may settle on a state that is against every member's private information. Even if every individual is able to identify the true state independently, the society may end up learning incorrectly after communications.
This paper studies a sequential social learning problem in which 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 or its perception, as suggested by the literature. When there is sufficient ambiguity, an information cascade occurs almost surely for all possible data-generating processes. This paper further shows that standard results that feature no cascade may be fragile to arbitrarily small ambiguity.
2. Naïve Social Learning with Heterogeneous Model Perceptions [PDF]
This paper studies a social learning problem where individuals observe a sequence of signals and repeatedly communicate their beliefs with neighbors. Individuals follow a naïve rule when learning from others and may incorrectly interpret their own information. This paper provides a set of characterizations for limit beliefs in this learning problem. One key feature of the characterizations is that the society has a tendency to settle on a state that minimizes the weighted relative entropy between the true and the perceived data-generating processes, and the weight describes the network's centrality. This paper further notes that it is possible that beliefs fail to converge or converge to multiple limits, which can be characterized by a variant of the weighted relative entropy. One implication is that group irrationality can arise. The society may settle on a state that is against every member's private information. Even if every individual is able to identify the true state independently, the society may end up learning incorrectly after communications.