Learning from Neighbors About a Changing State

74 Pages Posted: 10 Jan 2018 Last revised: 2 May 2022

See all articles by Krishna Dasaratha

Krishna Dasaratha

Yale University - Cowles Foundation

Benjamin Golub

Northwestern University

Nir Hak

Harvard University

Date Written: April 29, 2022

Abstract

Agents learn about a changing state using private signals and past actions of neighbors in a network. Bayesian learning in equilibrium yields a DeGroot-style learning dynamic, where agents use social information simply by averaging neighbors' recent estimates, with time-invariant weights. We examine when a community 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. Behavioral variations of the model demonstrate that achieving good aggregation requires a sophisticated response to correlations in neighbors' actions. Finally, we find that an agent's social influence is much more sensitive to the precision of her private signal than in the DeGroot benchmark.

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

JEL Classification: D85

Suggested Citation

Dasaratha, Krishna and Golub, Benjamin and Hak, Nir, Learning from Neighbors About a Changing State (April 29, 2022). 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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