Näıve Learning in Social Networks and the Wisdom of Crowds

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 näıvely 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.