Juan Pablo Xandri
Department of Economics
Fisher Hall, Office 212
Princeton, NJ 08544
NBER Working Papers and Publications
|August 2015||Testing Models of Social Learning on Networks: Evidence from a Lab Experiment in the Field|
with Arun G. Chandrasekhar, Horacio Larreguy: w21468
Agents often use noisy signals from their neighbors to update their beliefs about a state of the world. The effectiveness of social learning relies on the details of how agents aggregate information from others. There are two prominent models of information aggregation in networks: (1) Bayesian learning, where agents use Bayes' rule to assess the state of the world and (2) DeGroot learning, where agents instead consider a weighted average of their neighbors' previous period opinions or actions. Agents who engage in DeGroot learning often double-count information and may not converge in the long run. We conduct a lab experiment in the field with 665 subjects across 19 villages in Karnataka, India, designed to structurally test which model best describes social learning. Seven subjects were ...