Learning with Heterogeneous Misspecified Models: Characterization and Robustness

Aislinn Bohren and Daniel Hauser

No 12036, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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 misspecification. We present this characterization in the context of social learning, then highlight how it applies to other learning environments, 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 differing levels of bias or unawareness of others' biases impact learning, and explore whether the impact of a bias is sensitive to parametric specification or the source of information. This unified framework synthesizes insights gleaned from previously studied forms of misspecification and provides novel insights in specific applications, as we demonstrate in settings with partisan bias, overreaction, naive learning, and level-k reasoning.

Keywords: Model misspecification; Social learning (search for similar items in EconPapers)
JEL-codes: D82 D83 (search for similar items in EconPapers)
Date: 2017-05
New Economics Papers: this item is included in nep-evo and nep-mic
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Working Paper: Learning with Heterogeneous Misspecfied Models: Characterization and Robustness (2021)
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