Work

Social learning through coarse signals of others' actions

Year: 2025

Type: article

Abstract: This paper studies a sequential social learning model in which agents learn about an underlying state from others' actions. Unlike classic models, we consider a setting where agents may observe coarse signals of past actions. We identify a simple, necessary, and sufficient condition for asymptotic learning, called separability , which depends on both the information environment and the payoff structure. A necessary condition for separability is “unbounded beliefs” which requires agents' private information to generate strong evidence of the true state, even if only with small probabilities. We also identify conditions on the information environment alone that guarantee separability for all payoff structures. These conditions include unbounded beliefs and a new condition on agents' signals of others' actions, termed double thresholds . Without double thresholds, learning can be confounded so that agents always choose different actions with positive probabilities and never reach a consensus.

Source: Journal of Economic Theory

DOI: 10.1016/j.jet.2025.106066

Author: Wenji Xu

Institution: City University of Hong Kong

Language: English

Topic: Game Theory and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,030

Funders: City University of Hong Kong, Research Grants Council, University Grants Committee