Naïve learning in social networks with random communication

Jia Ping Huang; Bernd Heidergott; Ines Lindner

doi:10.1016/j.socnet.2019.01.004

Naïve learning in social networks with random communication

Jia Ping Huang^*, Bernd Heidergott, Ines Lindner

^*Corresponding author for this work

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Abstract

We study social learning in a social network setting where agents receive independent noisy signals about the truth. Agents naïvely update beliefs by repeatedly taking weighted averages of neighbors’ opinions. The weights are fixed in the sense of representing average frequency and intensity of social interaction. However, the way people communicate is random such that agents do not update their belief in exactly the same way at every point in time. Our findings, based on Theorem 1, Corollary 1 and simulated examples, suggest the following. Even if the social network does not privilege any agent in terms of influence, a large society almost always fails to converge to the truth. We conclude that wisdom of crowds seems an illusive concept and bares the danger of mistaking consensus for truth.

Original language	English
Pages (from-to)	1-11
Number of pages	11
Journal	Social Networks
Volume	58
Early online date	7 Feb 2019
DOIs	https://doi.org/10.1016/j.socnet.2019.01.004
Publication status	Published - Jul 2019

Funding

We gratefully acknowledge the financial support of NWO (grant 400-09-434 ) and the Department of Education of Guangdong Province, China (grant 2016WQNCX129 ).

Funders	Funder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek	400-09-434
Department of Education of Guangdong Province	2016WQNCX129

Keywords

Naïve learning
Social networks
Wisdom of crowds

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.socnet.2019.01.004

Naïve learning in social networks with random communicationFinal published version, 4.34 MBLicence: Article 25fa Dutch Copyright Act

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title = "Na{\"i}ve learning in social networks with random communication",

abstract = "We study social learning in a social network setting where agents receive independent noisy signals about the truth. Agents na{\"i}vely update beliefs by repeatedly taking weighted averages of neighbors{\textquoteright} opinions. The weights are fixed in the sense of representing average frequency and intensity of social interaction. However, the way people communicate is random such that agents do not update their belief in exactly the same way at every point in time. Our findings, based on Theorem 1, Corollary 1 and simulated examples, suggest the following. Even if the social network does not privilege any agent in terms of influence, a large society almost always fails to converge to the truth. We conclude that wisdom of crowds seems an illusive concept and bares the danger of mistaking consensus for truth.",

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year = "2019",

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AU - Huang, Jia Ping

AU - Heidergott, Bernd

AU - Lindner, Ines

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N2 - We study social learning in a social network setting where agents receive independent noisy signals about the truth. Agents naïvely update beliefs by repeatedly taking weighted averages of neighbors’ opinions. The weights are fixed in the sense of representing average frequency and intensity of social interaction. However, the way people communicate is random such that agents do not update their belief in exactly the same way at every point in time. Our findings, based on Theorem 1, Corollary 1 and simulated examples, suggest the following. Even if the social network does not privilege any agent in terms of influence, a large society almost always fails to converge to the truth. We conclude that wisdom of crowds seems an illusive concept and bares the danger of mistaking consensus for truth.

AB - We study social learning in a social network setting where agents receive independent noisy signals about the truth. Agents naïvely update beliefs by repeatedly taking weighted averages of neighbors’ opinions. The weights are fixed in the sense of representing average frequency and intensity of social interaction. However, the way people communicate is random such that agents do not update their belief in exactly the same way at every point in time. Our findings, based on Theorem 1, Corollary 1 and simulated examples, suggest the following. Even if the social network does not privilege any agent in terms of influence, a large society almost always fails to converge to the truth. We conclude that wisdom of crowds seems an illusive concept and bares the danger of mistaking consensus for truth.

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Naïve learning in social networks with random communication

Abstract

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Keywords

UN SDGs

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