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Full-Text Articles in Economics
Stability And Robustness In Misspecified Learning Models, Mira Frick, Ryota Iijima, Yuhta Ishii
Stability And Robustness In Misspecified Learning Models, Mira Frick, Ryota Iijima, Yuhta Ishii
Cowles Foundation Discussion Papers
We present an approach to analyze learning outcomes in a broad class of misspecified environments, spanning both single-agent and social learning. Our main results provide general criteria to determine—without the need to explicitly analyze learning dynamics—when beliefs in a given environment converge to some long-run belief either locally or globally (i.e., from some or all initial beliefs). The key ingredient underlying these criteria is a novel “prediction accuracy” ordering over subjective models that refines existing comparisons based on Kullback-Leibler divergence. We show that these criteria can be applied, first, to unify and generalize various convergence results in previously studied settings. …
Belief Convergence Under Misspecified Learning: A Martingale Approach, Mira Frick, Ryota Iijima, Yuhta Ishii
Belief Convergence Under Misspecified Learning: A Martingale Approach, Mira Frick, Ryota Iijima, Yuhta Ishii
Cowles Foundation Discussion Papers
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 …
Belief Convergence Under Misspecified Learning: A Martingale Approach, Mira Frick, Ryota Iijima, Yuhta Ishii
Belief Convergence Under Misspecified Learning: A Martingale Approach, Mira Frick, Ryota Iijima, Yuhta Ishii
Cowles Foundation Discussion Papers
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 …