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Belief Convergence under Misspecified Learning: A Martingale Approach

Mira Frick (), Ryota Iijima () and Yuhta Ishii ()
Additional contact information
Mira Frick: Cowles Foundation, Yale University, https://economics.yale.edu/people/faculty/mira-frick
Ryota Iijima: Cowles Foundation, Yale University, https://economics.yale.edu/people/faculty/ryota-iijima

No 2235R2, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: We present an approach to analyze learning outcomes in a broad class of misspecified environments, spanning both single-agent and social learning. We introduce a novel "prediction accuracy" order over subjective models, and observe that this makes it possible to partially restore standard martingale convergence arguments that apply under correctly specified learning. Based on this, we derive general conditions to determine when beliefs in a given environment converge to some long-run belief either locally or globally (i.e., from some or all initial beliefs). We show that these conditions can be applied, first, to unify and generalize various convergence results in previously studied settings. Second, they enable us to analyze environments where learning is "slow," such as costly information acquisition and sequential social learning. In such environments, we illustrate that even if agents learn the truth when they are correctly specified, vanishingly small amounts of misspecification can generate extreme failures of learning.

Keywords: Misspecified learning; Stability; Robustness; Berk-Nash equilibrium (search for similar items in EconPapers)
Pages: 55 pages
Date: 2020-05, Revised 2021-12
New Economics Papers: this item is included in nep-gth and nep-mic
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Related works:
Working Paper: Belief Convergence under Misspecified Learning: A Martingale Approach (2021) Downloads
Working Paper: Belief Convergence under Misspecified Learning: A Martingale Approach (2021) Downloads
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