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

Syngjoo Choi, Sanjeev Goyal (), Frederic Moisan () and Yu Yang Tony To ()
Additional contact information
Sanjeev Goyal: University of Cambridge, Cambridge, United Kingdom; New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
Frederic Moisan: Emlyon Business School, GATE UMR 5824, 69130 Ecully, France
Yu Yang Tony To: University of Cambridge, Cambridge, United Kingdom

Management Science, 2023, vol. 69, issue 5, 2778-2787

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: social learning; social networks; experimental social science; consensus (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2023.4680 (application/pdf)

Related works:
Working Paper: Learning in Networks: An Experiment on Large Networks with Real-World Features (2023)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:69:y:2023:i:5:p:2778-2787

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().