Biased Learning under Ambiguous Information

58 Pages Posted: 5 Feb 2021 Last revised: 22 Aug 2022

See all articles by Jaden Yang Chen

Jaden Yang Chen

University of North Carolina (UNC) at Chapel Hill

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 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.

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

JEL Classification: D81, D83, C72

Suggested Citation

Chen, Jaden Yang, Biased Learning under Ambiguous Information (December 15, 2020). Journal of Economic Theory, Vol. 203, No. 105492, 2022, Available at SSRN: or

Jaden Yang Chen (Contact Author)

University of North Carolina (UNC) at Chapel Hill ( email )

Chapel Hill, NC 27599
United States

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