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Provedor de dados:  AgEcon
País:  United States
Título:  Naive Learning in Social Networks: Convergence, Influence and Wisdom of Crowds
Autores:  Jackson, Matthew O.
Golub, Benjamin
Jackson, Matthew O.
Data:  2007-07-27
Ano:  2007
Palavras-chave:  Social Networks
Learning
Diffusion
Bounded Rationality
Institutional and Behavioral Economics
D85
D83
A14
L14
Z13
Resumo:  We study learning and influence in a setting where agents communicate according to an arbitrary social network and naively 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 naive 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.
Tipo:  Working or Discussion Paper
Idioma:  Inglês
Identificador:  26873

http://purl.umn.edu/9101
Editor:  AgEcon Search
Relação:  Fondazione Eni Enrico Mattei (FEEM)>Coalition Theory Network Working Papers
CTN Nota di Lavoro 64.2007
Formato:  46

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