Biased Learning under Ambiguous Information

54 Pages Posted: 5 Feb 2021 Last revised: 8 Apr 2021

See all articles by Jaden Yang Chen

Jaden Yang Chen

Cornell University, Department of Economics

Date Written: December 15, 2020


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 self-confirm 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. As an extension, this paper also discusses the case where the bias can change with beliefs.

Keywords: biased learning, model uncertainty, ambiguity, self-serving bias, confirmation bias

JEL Classification: D81, D83, C72

Suggested Citation

Chen, Jaden Yang, Biased Learning under Ambiguous Information (December 15, 2020). Available at SSRN: or

Jaden Yang Chen (Contact Author)

Cornell University, Department of Economics ( email )

Ithaca, NY
United States

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