Learning from Neighbors About a Changing State

75 Pages Posted: 10 Jan 2018 Last revised: 21 Jan 2020

See all articles by Krishna Dasaratha

Krishna Dasaratha

Yale University - Cowles Foundation

Benjamin Golub

Northwestern University

Nir Hak

Harvard University

Date Written: January 16, 2020


Abstract. Agents learn about a changing state using private signals and past actions of neighbors in a network. We characterize equilibrium learning and social influence in this setting. We then examine when agents can aggregate information well, responding quickly to recent changes. A key sufficient condition for good aggregation is that each individual's neighbors have sufficiently different types of private information. In contrast, when signals are homogeneous, aggregation is suboptimal on any network. We also examine behavioral versions of the model, and show that achieving good aggregation requires a sophisticated understanding of correlations in neighbors' actions. The model provides a Bayesian foundation for a tractable learning dynamic in networks, closely related to the DeGroot model, and offers new tools for counterfactual and welfare analyses.

Keywords: Social Learning, Bayesian Learning, DeGroot Model, Information Aggregation, Networks, Centrality

Suggested Citation

Dasaratha, Krishna and Golub, Benjamin and Hak, Nir, Learning from Neighbors About a Changing State (January 16, 2020). Available at SSRN: https://ssrn.com/abstract=3097505 or http://dx.doi.org/10.2139/ssrn.3097505

Krishna Dasaratha

Yale University - Cowles Foundation ( email )

Box 208281
New Haven, CT 06520-8281
United States

Benjamin Golub (Contact Author)

Northwestern University ( email )

Evanston, IL 60201
United States

Nir Hak

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

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