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

Author

Listed:
  • Syngjoo Choi
  • Sanjeev Goyal
  • Frederic Moisan

    (EM - EMLyon Business School)

  • Yu Yang Tony To

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," Post-Print hal-04325659, HAL.
    • Handle: RePEc:hal:journl:hal-04325659
    as

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    References listed on IDEAS

    as
    1. Gale, Douglas & Kariv, Shachar, 2003. "Bayesian learning in social networks," Games and Economic Behavior, Elsevier, vol. 45(2), pages 329-346, November.
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