Journal
MANAGEMENT SCIENCE
Volume 69, Issue 5, Pages 2778-2787Publisher
INFORMS
DOI: 10.1287/mnsc.2023.4680
Keywords
social learning; social networks; experimental social science; consensus
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This study investigates learning dynamics in three large-scale networks and finds that the Royal Family network is more likely to sustain incorrect consensus, while the Stochastic Block network is more likely to persist with diverse beliefs. These findings support the use of simple heuristics in information aggregation in large and complex networks, as predicted by DeGroot updating.
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: Erdo center dot s-Re ' nyi, 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.
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