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Faculty of Economics

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Choi, S., Goyal, S., Moisan, F. and To, Y. Y. T.

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

Management Science


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.

Keywords: consensus, experimental social science, social learning, social networks

JEL Codes: C91, C92, D83, D85

Author links: Sanjeev Goyal  

Publisher's Link:

Keynes Fund Project(s):
Social Learning and Fake News (JHUM)  

Cambridge Working Paper in Economics Version of Paper: Learning in Canonical Networks, Choi, S., Goyal, S., Moisan, F., To, Y. Y. T., (2022)

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