Biased learning under ambiguous information

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.