Social Learning in a Dynamic Environment

62 Pages Posted: 10 Jan 2018 Last revised: 2 Jun 2018

Krishna Dasaratha

Harvard University

Benjamin Golub

Harvard University

Nir Hak

Harvard University

Date Written: May 27, 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 environment? First, if private signal distributions are diverse enough across agents, then Bayesian learning achieves good information aggregation as long as individuals observe sufficiently many others. Second, without such diversity, Bayesian information aggregation can fall far short of good aggregation benchmarks, and can be Pareto-inefficient. Third, good aggregation requires anti-imitation; without it, agents' estimates are inefficiently confounded by "echoes." Our 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. The resulting tractability can facilitate structural estimation of equilibrium learning models and testing against behavioral alternatives, as well as the analysis of welfare and influence.

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 (May 27, 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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