Naïve learning in social networks with random communication
| Autor: | Ines Lindner, Jia Ping Huang, Bernd Heidergott |
|---|---|
| Přispěvatelé: | Econometrics and Operations Research, Amsterdam Business Research Institute, Tinbergen Institute |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
SDG 16 - Peace
050402 sociology Sociology and Political Science Computer science Social networks Corollary 0504 sociology Naïve learning 050602 political science & public administration Almost surely General Psychology Social network business.industry 05 social sciences SDG 16 - Peace Justice and Strong Institutions General Social Sciences Social learning Justice and Strong Institutions Social relation 0506 political science Anthropology Artificial intelligence Wisdom of crowds Information cascade business Weighted arithmetic mean Privilege (social inequality) |
| Zdroj: | Social Networks, 58, 1-11. Elsevier BV Huang, J P, Heidergott, B & Lindner, I 2019, ' Naïve learning in social networks with random communication ', Social Networks, vol. 58, pp. 1-11 . https://doi.org/10.1016/j.socnet.2019.01.004 |
| ISSN: | 0378-8733 |
| DOI: | 10.1016/j.socnet.2019.01.004 |
| Popis: | We study social learning in a social network setting where agents receive independent noisy signals about the truth. Agents naively 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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