The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Naïve Learning in Social Networks and the Wisdom of Crowds
Abstract
We study learning in a setting where agents receive independent noisy signals about the true value of a variable and then communicate in a network. They naïvely update beliefs by repeatedly taking weighted averages of neighbors' opinions. We show that all opinions in a large society converge to the truth if and only if the influence of the most influential agent vanishes as the society grows. We also identify obstructions to this, including prominent groups, and provide structural conditions on the network ensuring efficient learning. Whether agents converge to the truth is unrelated to how quickly consensus is approached. (JEL D83, D85, Z13)
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Cites
Matrix Analysis and Applied Linear Algebra
Meyer, C. (2000) · doi:10.1137/1.9780898719512 · Cited by 3,565
How social influence can undermine the wisdom of crowd effect
Lorenz, J., Rauhut, H., Schweitzer, F., & Helbing, D. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1008636108 · Cited by 1,017
Opinion Dynamics and Learning in Social Networks
Acemoglu, D., & Ozdaglar, A. (2011) · Dynamic Games and Applications · doi:10.1007/s13235-010-0004-1 · Cited by 705
Distributed multi-agent optimization with state-dependent communication
Lobel, I., Ozdaglar, A., & Feijer, D. (2011) · Mathematical Programming · doi:10.1007/s10107-011-0467-x · Cited by 162
Carbon Leakage With International Technology Spillovers
Gerlagh, R., & Kuik, O. (2007) · SSRN Electronic Journal · doi:10.2139/ssrn.979915 · Cited by 161
Cited by
How social influence can undermine the wisdom of crowd effect
Lorenz, J., Rauhut, H., Schweitzer, F., & Helbing, D. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1008636108 · Cited by 1,017
Fighting misinformation on social media using crowdsourced judgments of news source quality
Pennycook, G., & Rand, D. G. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1806781116 · Cited by 947
“Here, we consider an alternative approach that builds off the large literature on collective intelligence and the “ wisdom of crowds ” (10, 11): using crowdsourcing (rather than professional fact-checkers) to assess the reliability of news websites (rather than individual stories), and then…”
BackgroundBayesian Learning in Social Networks
Acemoglu, D., Dahleh, M. A., Lobel, I., & Ozdaglar, A. (2011) · The Review of Economic Studies · doi:10.1093/restud/rdr004 · Cited by 754
Opinion Dynamics and Learning in Social Networks
Acemoglu, D., & Ozdaglar, A. (2011) · Dynamic Games and Applications · doi:10.1007/s13235-010-0004-1 · Cited by 705
Spread of (mis)information in social networks
Acemoglu, D., Ozdaglar, A., & ParandehGheibi, A. (2010) · Games and Economic Behavior · doi:10.1016/j.geb.2010.01.005 · Cited by 444
Metadata
- Authors
- Golub, B., & Jackson, M. O.
- Venue
- American Economic Journal: Microeconomics
- Publisher
- American Economic Association
- Published
- 2010-02-01
- Volume
- 2
- Issue
- 1
- Pages
- 112-149
- ISSN
- 1945-7669 (ISSN-L)
- 1945-7685
- OpenAlex
- W2086645750
- Cited by
- 979
- References
- 5
- FWCI
- 80.23
- Type
- journal article
- Language
- en
- Open access
- closed
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