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Learning Dynamics in Social Networks

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  • Simon Board
  • Moritz Meyer‐ter‐Vehn

Abstract

This paper proposes a tractable model of Bayesian learning on large random networks where agents choose whether to adopt an innovation. We study the impact of the network structure on learning dynamics and product diffusion. In directed networks, all direct and indirect links contribute to agents' learning. In comparison, learning and welfare are lower in undirected networks and networks with cliques. In a rich class of networks, behavior is described by a small number of differential equations, making the model useful for empirical work.

Suggested Citation

  • Simon Board & Moritz Meyer‐ter‐Vehn, 2021. "Learning Dynamics in Social Networks," Econometrica, Econometric Society, vol. 89(6), pages 2601-2635, November.
    • Handle: RePEc:wly:emetrp:v:89:y:2021:i:6:p:2601-2635
    DOI: 10.3982/ECTA18659
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    References listed on IDEAS

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    Cited by:

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    2. John Higgins & Tarun Sabarwal, 2023. "Control and spread of contagion in networks with global effects," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 25(6), pages 1149-1187, December.
    3. John Higgins & Tarun Sabarwal, 2021. "Control and Spread of Contagion in Networks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202201, University of Kansas, Department of Economics, revised Jan 2022.
    4. Julian Hidalgo & Michelle Sovinsky, 2023. "Internet (Power) to the People: How to Bridge the Digital Divide," CRC TR 224 Discussion Paper Series crctr224_2023_461, University of Bonn and University of Mannheim, Germany.
    5. Aloosh, Arash & Choi, Hyung-Eun & Ouzan, Samuel, 2023. "The tail wagging the dog: How do meme stocks affect market efficiency?," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 68-78.

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