Beyond Unbounded Beliefs: How Preferences and Information Interplay in Social Learning

Navin Kartik, SangMok Lee, Tianhao Liu and Daniel Rappoport

Papers from arXiv.org

Abstract: When does society eventually learn the truth, or take the correct action, via observational learning? In a general model of sequential learning over social networks, we identify a simple condition for learning dubbed excludability. Excludability is a joint property of agents' preferences and their information. We develop two classes of preferences and information that jointly satisfy excludability: (i) for a one-dimensional state, preferences with single-crossing differences and a new informational condition, directionally unbounded beliefs; and (ii) for a multi-dimensional state, intermediate preferences and subexponential location-shift information. These applications exemplify that with multiple states "unbounded beliefs" is not only unnecessary for learning, but incompatible with familiar informational structures like normal information. Unbounded beliefs demands that a single agent can identify the correct action. Excludability, on the other hand, only requires that a single agent must be able to displace any wrong action, even if she cannot take the correct action.

Date: 2021-03, Revised 2024-04
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Citations: View citations in EconPapers (4)

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Journal Article: Beyond Unbounded Beliefs: How Preferences and Information Interplay in Social Learning (2024)
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