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

Syngjoo Choi, Sanjeev Goyal, Frederic Moisan, Yu Yang Tony To

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)2778-2787
Number of pages10
JournalManagement Science
Issue number5
StatePublished - May 2023


  • consensus
  • experimental social science
  • social learning
  • social networks

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research


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