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

Syngjoo Choi, Douglas Gale, Shachar Kariv

    Research output: Contribution to journalArticle

    Abstract

    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.

    Original languageEnglish (US)
    Pages (from-to)135-157
    Number of pages23
    JournalReview of Economic Design
    Volume16
    Issue number2-3
    DOIs
    StatePublished - Sep 2012

    Keywords

    • Experiment
    • Quantal Response Equilibrium (QRE)
    • Social learning
    • Social networks

    ASJC Scopus subject areas

    • Economics, Econometrics and Finance(all)

    Fingerprint Dive into the research topics of 'Social learning in networks: A Quantal Response Equilibrium analysis of experimental data'. Together they form a unique fingerprint.

    Cite this