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Learning in Networks: An Experiment on Large Networks with Real-World Features

Author

Listed:
  • Syngjoo Choi

    (Department of Economics, Seoul National University, Seoul 08826, Republic of Korea)

  • Sanjeev Goyal

    (University of Cambridge, Cambridge, United Kingdom; New York University Abu Dhabi, Abu Dhabi, United Arab Emirates)

  • Frederic Moisan

    (Emlyon Business School, GATE UMR 5824, 69130 Ecully, France)

  • Yu Yang Tony To

    (University of Cambridge, Cambridge, United Kingdom)

Abstract

Subjects observe a private signal and make an initial guess; they then observe their neighbors’ guesses, update their own guess, and so forth. We study learning dynamics in three large-scale networks capturing features of real-world social networks: Erdös–Rényi, Stochastic Block (reflecting network homophily), and Royal Family (that accommodates both highly connected celebrities and local interactions). We find that the Royal Family network is more likely to sustain incorrect consensus and that the Stochastic Block network is more likely to persist with diverse beliefs. These patterns are consistent with the predictions of DeGroot updating. It lends support to the notion that the use of simple heuristics in information aggregation is prevalent in large and complex networks.

Suggested Citation

  • Syngjoo Choi & Sanjeev Goyal & Frederic Moisan & Yu Yang Tony To, 2023. "Learning in Networks: An Experiment on Large Networks with Real-World Features," Management Science, INFORMS, vol. 69(5), pages 2778-2787, May.
    • Handle: RePEc:inm:ormnsc:v:69:y:2023:i:5:p:2778-2787
    DOI: 10.1287/mnsc.2023.4680
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    References listed on IDEAS

    as
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