Naïve Learning in Social Networks: Convergence, Influence and Wisdom of Crowds

Matthew Jackson and Benjamin Golub

No 2007.64, Working Papers from Fondazione Eni Enrico Mattei

Abstract: We study learning and influence in a setting where agents communicate according to an arbitrary social network and naïvely update their beliefs by repeatedly taking weighted averages of their neighbors’ opinions. A focus is on conditions under which beliefs of all agents in large societies converge to the truth, despite their naïve updating. We show that this happens if and only if the influence of the most influential agent in the society is vanishing as the society grows. Using simple examples, we identify two main obstructions which can prevent this. By ruling out these obstructions, we provide general structural conditions on the social network that are sufficient for convergence to truth. In addition, we show how social influence changes when some agents redistribute their trust, and we provide a complete characterization of the social networks for which there is a convergence of beliefs. Finally, we survey some recent structural results on the speed of convergence and relate these to issues of segregation, polarization and propaganda.

Keywords: Social Networks; Learning; Diffusion; Bounded Rationality (search for similar items in EconPapers)
JEL-codes: A14 D83 D85 L14 Z13 (search for similar items in EconPapers)
Date: 2007-06
New Economics Papers: this item is included in nep-cbe, nep-evo, nep-net and nep-soc
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Naive Learning in Social Networks: Convergence, Influence and Wisdom of Crowds (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:fem:femwpa:2007.64

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