Learning from Neighbors About a Changing State

67 Pages Posted: 10 Jan 2018 Last revised: 6 Mar 2023

See all articles by Krishna Dasaratha

Krishna Dasaratha

Boston University

Benjamin Golub

Northwestern University

Nir Hak

Uber Technologies, Inc.

Date Written: March 5, 2023

Abstract

Agents learn about a changing state using private signals and their neighbors' past estimates of the state. We present a model in which Bayesian agents in equilibrium use neighbors' estimates simply by taking weighted sums with time-invariant weights. The dynamics thus parallel those of the tractable DeGroot model of learning in networks, but arise as an equilibrium outcome rather than a behavioral assumption. We examine whether information aggregation is nearly optimal as neighborhoods grow large. A key condition for this is signal diversity: each individual's neighbors have private signals that not only contain independent information, but also have sufficiently different distributions. Without signal diversity---e.g., if private signals are i.i.d.---learning is suboptimal in all networks and highly inefficient in some. Turning to social influence, we find it is much more sensitive to one's signal quality than to one's number of neighbors, in contrast to standard models with exogenous updating rules.

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 (March 5, 2023). Available at SSRN: https://ssrn.com/abstract=3097505 or http://dx.doi.org/10.2139/ssrn.3097505

Krishna Dasaratha

Boston University ( email )

595 Commonwealth Avenue
Boston, MA 02215
United States

Benjamin Golub (Contact Author)

Northwestern University ( email )

Evanston, IL 60201
United States

Nir Hak

Uber Technologies, Inc. ( email )

San Francisco, CA 94158

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