Social Learning in a Dynamic Environment

66 Pages Posted: 10 Jan 2018 Last revised: 26 Aug 2018

See all articles by Krishna Dasaratha

Krishna Dasaratha

Harvard University

Benjamin Golub

Harvard University

Nir Hak

Harvard University

Date Written: July 26, 2018

Abstract

Agents learn about a state using private signals and the past actions of their neighbors. In contrast to most models of social learning in a network, the target being learned about is moving around. We ask: when can a group aggregate information quickly, keeping up with the changing state? First, if each agent has access to neighbors with sufficiently diverse kinds of signals, then Bayesian learning achieves good information aggregation. Second, without such diversity, there are cases in which Bayesian information aggregation necessarily falls far short of efficient benchmarks. Third, good aggregation can be achieved only if agents “anti-imitate” some neighbors: otherwise, equilibrium estimates are inefficiently confounded by “echoes.” Agents’ stationary equilibrium learning rules incorporate past information by taking linear combinations of other agents’ past estimates (as in the simple DeGroot heuristic), and we characterize the coefficients in these linear combinations. We discuss how the resulting tractability is useful for structural estimation of equilibrium learning models and testing against behavioral alternatives.

Keywords: social learning, Bayesian learning, DeGroot model, information aggregation, networks, centrality

Suggested Citation

Dasaratha, Krishna and Golub, Benjamin and Hak, Nir, Social Learning in a Dynamic Environment (July 26, 2018). Available at SSRN: https://ssrn.com/abstract=3097505 or http://dx.doi.org/10.2139/ssrn.3097505

Krishna Dasaratha

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Benjamin Golub (Contact Author)

Harvard University ( email )

Littauer Center, Dept of Economics
1805 Cambridge Street
Cambridge, MA 02138
United States

Nir Hak

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

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