Testing Models of Social Learning on Networks: Evidence from a Lab Experiment in the Field

60 Pages Posted: 18 Aug 2015 Last revised: 28 Feb 2021

See all articles by Arun G. Chandrasekhar

Arun G. Chandrasekhar

Stanford University - Department of Economics

Horacio Larreguy

Harvard University

Juan Pablo Xandri

Princeton University

Date Written: August 2015


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 placed into a network with common knowledge of the network structure. Subjects attempted to learn the underlying (binary) state of the world, having received independent identically distributed signals in the first period. Thereafter, in each period, subjects made guesses about the state of the world, and these guesses were transmitted to their neighbors at the beginning of the following round. We structurally estimate a model of Bayesian learning, relaxing common knowledge of Bayesian rationality by allowing agents to have incomplete information as to whether others are Bayesian or DeGroot. Our estimates show that, despite the flexibility in modeling learning in these networks, agents are robustly best described by DeGroot-learning models wherein they take a simple majority of previous guesses in their neighborhood.

Suggested Citation

Chandrasekhar, Arun G. and Larreguy, Horacio and Xandri, Juan Pablo, Testing Models of Social Learning on Networks: Evidence from a Lab Experiment in the Field (August 2015). NBER Working Paper No. w21468, Available at SSRN: https://ssrn.com/abstract=2645564

Arun G. Chandrasekhar (Contact Author)

Stanford University - Department of Economics ( email )

Landau Economics Building
579 Serra Mall
Stanford, CA 94305-6072
United States

Horacio Larreguy

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Juan Pablo Xandri

Princeton University ( email )

22 Chambers Street
Princeton, NJ 08544-0708
United States

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