Social Learning in Networks: Theory and Experiments

62 Pages Posted: 21 Sep 2013 Last revised: 1 Aug 2014

See all articles by Manuel Mueller-Frank

Manuel Mueller-Frank

University of Navarra, IESE Business School

Claudia Neri

University of St. Gallen

Date Written: December 27, 2013

Abstract

This paper presents a non-Bayesian model of social learning in networks in an environment with a finite set of actions. We conduct a laboratory experiment in which participants play an urn-guessing game over several decision rounds while observing the previous choices of the network members to whom they are connected. We identify three properties of individual choice revision: consistency, monotonicity and identity independence. We consider the class of revision functions satisfying such properties and establish that consensus occurs in arbitrary strongly connected networks if and only if the revision functions of all agents are identical and preference-based. Thus, consensus is hard to achieve, which is supported by evidence from our experiment. The theoretical prediction differs sharply from the existing results in the Bayesian and non-Bayesian literature.

Keywords: social learning, networks, experiments, consensus, information aggregation, herding

JEL Classification: C91, C92, D83, D85

Suggested Citation

Mueller-Frank, Manuel and Neri, Claudia, Social Learning in Networks: Theory and Experiments (December 27, 2013). Available at SSRN: https://ssrn.com/abstract=2328281 or http://dx.doi.org/10.2139/ssrn.2328281

Manuel Mueller-Frank (Contact Author)

University of Navarra, IESE Business School ( email )

Avenida Pearson 21
Barcelona, 08034
Spain

Claudia Neri

University of St. Gallen ( email )

Varnb├╝elstrasse 19
St. Gallen, CH-9000
Switzerland
+41-71-224-2757 (Phone)

HOME PAGE: http://www.claudianeri.com

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