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

• 成果类型: Article
• 署名作者: Choi, Syngjoo; Goyal, Sanjeev; Moisan, Frederic; To, Yu Yang Tony
• 署名单位: Seoul National University (SNU); University of Cambridge; New York University; New York University Abu Dhabi; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Humanities & Social Sciences (INSHS); Ecole Normale Superieure de Lyon (ENS de LYON); Universite Claude Bernard Lyon 1; Universite Jean Monnet; Universite Lyon 2; emlyon business school
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.4680
• 发表日期: 2023
• 页码: 2778-2787
• 关键词: Social learning; social networks; experimental social science; consensus

摘要

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.