Clustering in communication networks with different-minded participants

Abstract

This paper examines how the structure of communication networks influences learning and social welfare when participants have different prior opinions and face uncertainty about an external state. We analyze a game in which players form links to exchange opinions on the state and reduce their uncertainty. The players hold imperfectly correlated subjective priors on the state. Therefore, their opinions transmit their private signals with frictions, termed interpretation noise. Network clustering facilitates learning by eliminating this interpretation noise. Therefore, the egalitarian efficient network is: a complete component if the interpretation noise is sufficiently high, and a flower otherwise. This network constitutes a Nash equilibrium. These findings establish a link between a key feature of social networks (clustering) and the quality of learning through network communication, offering a potential explanation for the prevalence of clustering in real-world social networks.

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Reference

Thibault Laurent, and Elena Panova, “Clustering in communication networks with different-minded participants”, TSE Working Paper, n. 20-1147, September 2020, revised August 2024.