A multi-agent model of misspecified learning with overconfidence

Cuimin Ba and Alice Gindin

Games and Economic Behavior, 2023, vol. 142, issue C, 315-338

Abstract: This paper studies the long-term interaction between two overconfident agents who choose how much effort to exert while learning about their environment. Overconfidence causes agents to underestimate either a common fundamental, such as the underlying quality of their project, or their counterpart's ability, to justify their worse-than-expected performance. We show that in many settings, agents create informational externalities for each other. When informational externalities are positive, the agents' learning processes are mutually-reinforcing: one agent best responding to his own overconfidence causes the other agent to reach a more distorted belief and take more extreme actions, generating a positive feedback loop. The opposite pattern, mutually-limiting learning, arises when informational externalities are negative. We also show that in our multi-agent environment, overconfidence can lead to Pareto improvement in welfare. Finally, we prove that under certain conditions, agents' beliefs and effort choices converge to a steady state that is a Berk-Nash equilibrium.

Keywords: Overconfidence; Misspecified learning; Multiple agents; Informational externalities (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:gamebe:v:142:y:2023:i:c:p:315-338

DOI: 10.1016/j.geb.2023.08.007

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