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. (JEL D83, D85, Z13)
"Naïve Learning in Social Networks and the Wisdom of Crowds."
American Economic Journal: Microeconomics,
Search; Learning; Information and Knowledge; Communication; Belief
Network Formation and Analysis: Theory
Economic Sociology; Economic Anthropology; Social and Economic Stratification