Learning with Heterogeneous Misspecfied Models: Characterization and Robustness

Aislinn Bohren and Daniel N. Hauser ()
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Daniel N. Hauser: Aalto University and Helsinki GSE

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: This paper develops a general framework to study how misinterpreting information impacts learning. Our main result is a simple criterion to characterize long-run beliefs based on the underlying form of misspeci?cation. We present this characterization in the context of social learning, then highlight how it applies to other learning environ-ments, including individual learning. A key contribution is that our characterization applies to settings with model heterogeneity and provides conditions for entrenched disagreement. Our characterization can be used to determine whether a representative agent approach is valid in the face of heterogeneity, study how di?ering levels of bias or unawareness of others’ biases impact learning, and explore whether the impact of a bias is sensitive to parametric speci?cation or the source of information. This uni?ed framework synthesizes insights gleaned from previously studied forms of misspeci?ca-tion and provides novel insights in speci?c applications, as we demonstrate in settings with partisan bias, overreaction, naive learning, and level-k reasoning.

Keywords: Model misspecication; Social learning (search for similar items in EconPapers)
JEL-codes: C73 D83 (search for similar items in EconPapers)
Pages: 82 pages
Date: 2021-02-03
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Citations: View citations in EconPapers (33)

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Working Paper: Learning with Heterogeneous Misspecified Models: Characterization and Robustness (2017)
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