Naive Learning in Social Networks with Random Communication

Bernd (B.) Heidergott (), Jia-Ping Huang () and Ines (I.) Lindner ()
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Bernd (B.) Heidergott: VU Amsterdam, The Netherlands
Jia-Ping Huang: Shenzhen University, China
Ines (I.) Lindner: VU Amsterdam, The Netherlands

No 18-018/II, Tinbergen Institute Discussion Papers from Tinbergen Institute

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. We show that 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 is an illusive concept and bares the danger of mistaking consensus for truth.

Keywords: Wisdom of crowds; social networks; information cascades; naive learning (search for similar items in EconPapers)
JEL-codes: C63 D83 D85 (search for similar items in EconPapers)
Date: 2018-02-28
New Economics Papers: this item is included in nep-cdm, nep-gth, nep-mic and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20180018

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