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On the Efficiency of Social Learning

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  • Dinah Rosenberg
  • Nicolas Vieille

Abstract

We revisit prominent learning models in which a sequence of agents make a binary decision on the basis of both a private signal and information related to past choices. We analyze the efficiency of learning in these models, measured in terms of the expected welfare. We show that, irrespective of the distribution of private signals, learning efficiency is the same whether each agent observes the entire sequence of earlier decisions or only the previous decision. In addition, we provide a simple condition on the signal distributions that is necessary and sufficient for learning efficiency. This condition fails to hold in many cases of interest. We discuss a number of extensions and variants.

Suggested Citation

  • Dinah Rosenberg & Nicolas Vieille, 2019. "On the Efficiency of Social Learning," Econometrica, Econometric Society, vol. 87(6), pages 2141-2168, November.
    • Handle: RePEc:wly:emetrp:v:87:y:2019:i:6:p:2141-2168
    DOI: 10.3982/ECTA15845
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    Citations

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    Cited by:

    1. Mira Frick & Ryota Iijima & Yuhta Ishii, 2021. "Learning Efficiency of Multi-Agent Information Structures," Cowles Foundation Discussion Papers 2299, Cowles Foundation for Research in Economics, Yale University.
    2. Sushil Bikhchandani & David Hirshleifer & Omer Tamuz & Ivo Welch, 2024. "Information Cascades and Social Learning," Journal of Economic Literature, American Economic Association, vol. 62(3), pages 1040-1093, September.
    3. Wanying Huang & Philipp Strack & Omer Tamuz, 2024. "Learning in Repeated Interactions on Networks," Econometrica, Econometric Society, vol. 92(1), pages 1-27, January.
    4. Krishna Dasaratha & Kevin He, 2019. "Aggregative Efficiency of Bayesian Learning in Networks," Papers 1911.10116, arXiv.org, revised Sep 2024.
    5. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Belief Convergence under Misspecified Learning: A Martingale Approach," Cowles Foundation Discussion Papers 2235R2, Cowles Foundation for Research in Economics, Yale University, revised Dec 2021.
    6. Caio Lorecchio, 2022. "Persuading crowds," UB School of Economics Working Papers 2022/434, University of Barcelona School of Economics.
    7. 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.
    8. Xuanye Wang, 2021. "Fragility of Confounded Learning," Papers 2106.07712, arXiv.org.
    9. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Belief Convergence under Misspecified Learning: A Martingale Approach," Cowles Foundation Discussion Papers 2235R3, Cowles Foundation for Research in Economics, Yale University, revised Apr 2022.
    10. Zhang, Min, 2021. "Non-monotone social learning," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 565-579.
    11. Mira Frick & Ryota Iijima & Yuhta Ishii, 2021. "Learning Efficiency of Multi-Agent Information Structures," Cowles Foundation Discussion Papers 2299R2, Cowles Foundation for Research in Economics, Yale University, revised Jul 2022.
    12. Chen, Fengwen & Wang, Bing & Wang, Wei & Hu, Chen, 2024. "The secret of imitating wrongdoing: Accidental or deliberate," Research in International Business and Finance, Elsevier, vol. 69(C).

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