Naive Learning in Social Networks and the Wisdom of Crowds

被引：563

作者：

Golub, Benjamin ^{[1
]}

Jackson, Matthew O. ^{[2
,3
]}

机构：

[1] Stanford Univ, Grad Sch Business, Stanford, CA 94305 USA

[2] Stanford Univ, Dept Econ, Stanford, CA 94305 USA

[3] Santa Fe Inst, Santa Fe, NM USA

来源：

AMERICAN ECONOMIC JOURNAL-MICROECONOMICS | 2010年 / 2卷 / 01期

基金：

美国国家科学基金会;

关键词：

INFORMATIONAL CASCADES; HERD BEHAVIOR; CENTRALITY; POWER;

D O I：

10.1257/mic.2.1.112

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

We study learning in a setting where agents receive independent noisy signals about the true value of a variable and then communicate in a network. They naively update belief's by repeatedly taking weighted averages of neighbors' opinions. We show that all opinions in a large society converge to the truth if and only if the influence of the most influential agent vanishes as the society grows. We also identify obstructions to this, including prominent groups, and provide structural conditions on the network ensuring efficient learning. Whether agents converge to the truth is unrelated to how quickly consensus is approached. (JEL D83, D85, Z13)

引用

页码：112 / 149

页数：38

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