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Open Access Publications from the University of California

A Theory and Experiments of Learning in Social Networks

  • Author(s): Kariv, Shachar
  • et al.

Individuals living in society are bound together by a social network, the complex of relationships that brings them into contact with other agents. 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 agents have different information sets, 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. Whether individuals can rationally process the information available in a network is ultimately an empirical question. This paper reports an experimental investigation of learning in three-person networks and uses the theoretical framework Gale and Kariv (2003) to interpret the data generated by the experiments. The family of three-person networks includes several nontrivial architectures, each of which gives rise to its own distinctive learning patterns. We find that the theory can account for the behavior observed in the laboratory in variety of networks and informational settings. To account for errors in subjects’ behavior, we adapt the model of Quantal Response Equilibrium of McKelvey and Palfrey (1995, 1998) and find that its restrictions are also confirmed. The ‘goodness of fit’ is better for the QRE model than for the game-theory model. This provides important support for the use of QRE to interpret experimental data.

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