A multi-agent model of misspecified learning with overconfidence

DOI10.1016/j.geb.2023.08.007zbMath1530.91078OpenAlexW4385989071MaRDI QIDQ6148382

Cuimin Ba, Alice Gindin

Publication date: 11 January 2024

Published in: Games and Economic Behavior (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.geb.2023.08.007

zbMATH Keywords

multiple agentsoverconfidencemisspecified learninginformational externalities

Mathematics Subject Classification ID

Rationality and learning in game theory (91A26)

Cites Work

• Informational herding with model misspecification
• Learning in mis-specified models and the possibility of cycles
• Asymptotic behavior of Bayesian learners with misspecified models
• What to maximize if you must
• Learning With Heterogeneous Misspecified Models: Characterization and Robustness
• First Impressions Matter: A Model of Confirmatory Bias
• Berk-Nash Equilibrium: A Framework for Modeling Agents With Misspecified Models
• Unrealistic Expectations and Misguided Learning
• Active learning with a misspecified prior
• Misinterpreting Others and the Fragility of Social Learning
• Convergence in models of misspecified learning
• The Positive Effects of Biased Self-Perceptions in Firms*
• Limit Points of Endogenous Misspecified Learning
• Mislearning from censored data: The gambler's fallacy and other correlational mistakes in optimal‐stopping problems
• Belief Convergence under Misspecified Learning: A Martingale Approach

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