Naive Learning in Social Networks and the Wisdom of Crowds
Naive Learning in Social Networks and the Wisdom of Crowds
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DOI：
10.1257/mic.2.1.112
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发表时间：
2010-02-01
影响因子：
2.4
通讯作者：
Jackson, Matthew O.
中科院分区：
文献类型：
--
作者：
Golub, Benjamin;Jackson, Matthew O.
我们在代理商接收有关变量真实价值然后在网络中进行通信的独立噪声信号的环境中学习学习。他们通过反复采用邻居意见的加权平均值来天真地更新信念。我们表明，当且仅当随着社会的发展而消失时，大社会中的所有观点都融合了真理。我们还确定了包括杰出群体在内的障碍，并在网络上提供结构性条件，以确保有效学习。特工是否融合了真理与达成共识的速度无关。 （Jel D83，D85，Z13）
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)