Arbeitspapier
Naïve Learning in Social Networks with Random Communication
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.
- Language
-
Englisch
- Bibliographic citation
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Series: Tinbergen Institute Discussion Paper ; No. TI 2018-018/II
- Classification
-
Wirtschaft
Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
Network Formation and Analysis: Theory
Computational Techniques; Simulation Modeling
- Subject
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Wisdom of crowds
social networks
information cascades
naive learning
- Event
-
Geistige Schöpfung
- (who)
-
Heidergott, Bernd
Huang, Jiaping
Lindner, Ines
- Event
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Veröffentlichung
- (who)
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Tinbergen Institute
- (where)
-
Amsterdam and Rotterdam
- (when)
-
2018
- Handle
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
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Object type
- Arbeitspapier
Associated
- Heidergott, Bernd
- Huang, Jiaping
- Lindner, Ines
- Tinbergen Institute
Time of origin
- 2018
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