Naïve Learning in Social Networks and the Wisdom of Crowds
研究了在社交网络中，个体通过反复取邻居意见的加权平均来更新信念，发现当最有影响力的个体影响力随社会规模增大而消失时，所有意见会收敛到真实值，并指出了阻碍这一过程的因素。
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 naïvely update beliefs 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.