, Volume 16, Issue 2-3, pp 135-157
Date: 26 May 2012

Social learning in networks: a Quantal Response Equilibrium analysis of experimental data

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Individuals living in society are bound together by a social network and, in many social and economic situations, individuals learn by observing the behavior of others in their local environment. This process is called social learning. Learning in incomplete networks, where different individuals have different information, is especially challenging: because of the lack of common knowledge individuals must draw inferences about the actions others have observed, as well as about their private information. This paper reports an experimental investigation of learning in three-person networks and uses the theoretical framework of Gale and Kariv (Games Econ Behav 45:329–346, 2003) to interpret the data generated by the experiments. The family of three-person networks includes several non-trivial architectures, each of which gives rise to its own distinctive learning patterns. To test the usefulness of the theory in interpreting the data, we adapt the Quantal Response Equilibrium (QRE) model of Mckelvey and Palfrey (Games Econ Behav 10:6–38, 1995; Exp Econ 1:9–41, 1998). We find that the theory can account for the behavior observed in the laboratory in a variety of networks and informational settings. This provides important support for the use of QRE to interpret experimental data.

The results reported here were previously distributed in a paper titled “Learning in Networks: An Experimental Study.” This research was supported by the Center for Experimental Social Sciences (C.E.S.S.) and the C. V. Starr Center for Applied Economics at New York University. We thank Colin Camerer, Boğaçhan Çelen, Gary Charness, Xiaohong Chen, Charlie Holt, John Morgan, Tom Palfrey, Matthew Rabin, Andrew Schotter, and Georg Weizsäcker for helpful discussions. This paper has also benefited from suggestions by the participants of seminars at several universities. For financial support, Gale acknowledges the National Science Foundation Grant No. SBR-0095109 and the C. V. Starr Center for Applied Economics at New York University, and Kariv thanks the University of California, Berkeley (COR Grant).