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

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  • Syngjoo Choi

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  • Douglas Gale

    ()

  • Shachar Kariv

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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. Copyright Springer-Verlag 2012

Suggested Citation

  • Syngjoo Choi & Douglas Gale & Shachar Kariv, 2012. "Social learning in networks: a Quantal Response Equilibrium analysis of experimental data," Review of Economic Design, Springer;Society for Economic Design, vol. 16(2), pages 135-157, September.
  • Handle: RePEc:spr:reecde:v:16:y:2012:i:2:p:135-157
    DOI: 10.1007/s10058-012-0122-x
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    Cited by:

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    2. Jackson, Matthew O. & Zenou, Yves, 2015. "Games on Networks," Handbook of Game Theory with Economic Applications,, Elsevier.
    3. Hahn, Youjin & Islam, Asadul & Patacchini, Eleonora & Zenou, Yves, 2015. "Network Structure and Education Outcomes: Evidence from a Field Experiment in Bangladesh," IZA Discussion Papers 8872, Institute of Labor Economics (IZA).
    4. Fã–Rster, Manuel & Mauleon, Ana & Vannetelbosch, Vincent J., 2016. "Trust and manipulation in social networks," Network Science, Cambridge University Press, vol. 4(2), pages 216-243, June.
    5. Antinyan, Armenak & Horváth, Gergely & Jia, Mofei, 2020. "Positional concerns and social network structure: An experiment," European Economic Review, Elsevier, vol. 129(C).
    6. Zhang, Boyu & Hofbauer, Josef, 2016. "Quantal response methods for equilibrium selection in 2×2 coordination games," Games and Economic Behavior, Elsevier, vol. 97(C), pages 19-31.
    7. Ivan S Menshikov & Alexsandr V Shklover & Tatiana S Babkina & Mikhail G Myagkov, 2017. "From rationality to cooperativeness: The totally mixed Nash equilibrium in Markov strategies in the iterated Prisoner’s Dilemma," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-17, November.
    8. Levy, Gilat & Razin, Ronny, 2018. "Information diffusion in networks with the Bayesian Peer Influence heuristic," LSE Research Online Documents on Economics 86554, London School of Economics and Political Science, LSE Library.
    9. He, Simin & Wu, Jiabin, 2020. "Compromise and coordination: An experimental study," Games and Economic Behavior, Elsevier, vol. 119(C), pages 216-233.
    10. Michel Grabisch & Agnieszka Rusinowska, 2020. "A Survey on Nonstrategic Models of Opinion Dynamics," Games, MDPI, Open Access Journal, vol. 11(4), pages 1-29, December.
    11. Corazzini, Luca & Pavesi, Filippo & Petrovich, Beatrice & Stanca, Luca, 2012. "Influential listeners: An experiment on persuasion bias in social networks," European Economic Review, Elsevier, vol. 56(6), pages 1276-1288.
    12. Ganga Shreedhar, Alessandro Tavoni, Carmen Marchiori, 2018. "Monitoring and punishment networks in a common-pool resource dilemma: experimental evidence," GRI Working Papers 292, Grantham Research Institute on Climate Change and the Environment.
    13. Levy, Gilat & Razin, Ronny, 2018. "Information diffusion in networks with the Bayesian Peer Influence heuristic," Games and Economic Behavior, Elsevier, vol. 109(C), pages 262-270.
    14. Yann Algan & Quoc-Anh Do & Nicolò Dalvit & Alexis Le Chapelain & Yves Zenou, 2015. "How Social Networks Shape Our Beliefs: A Natural Experiment among Future French Politicians," Sciences Po publications info:hdl:2441/78vacv4udu9, Sciences Po.
    15. Syngjoo Choi & Edoardo Gallo & Shachar Kariv, 2015. "Networks in the laboratory," Cambridge Working Papers in Economics 1551, Faculty of Economics, University of Cambridge.
    16. Arun G. Chandrasekhar & Horacio Larreguy & Juan Pablo Xandri, 2020. "Testing Models of Social Learning on Networks: Evidence From Two Experiments," Econometrica, Econometric Society, vol. 88(1), pages 1-32, January.
    17. Lisa BREGER & Andrea SORENSEN, 2019. "Posted offers in exogenous networks: A theoretical application and experimental results," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(2(619), S), pages 21-46, Summer.
    18. Youjin Hahn & Asadul Islam & Eleonora Patacchini & Yves Zenou, 2015. "Teams, Organization and Education Outcomes: Evidence from a field experiment in Bangladesh," Monash Economics Working Papers 35-15, Monash University, Department of Economics.
    19. Jackson, Matthew O. & Golub, Benjamin, 2007. "Naive Learning in Social Networks: Convergence, Influence and Wisdom of Crowds," Coalition Theory Network Working Papers 9101, Fondazione Eni Enrico Mattei (FEEM).
    20. Mobius, Markus & Phan, Tuan & Szeidl, Adam, 2015. "Treasure Hunt: Social Learning in the Field," CEPR Discussion Papers 10493, C.E.P.R. Discussion Papers.
    21. Gressani, O., 2015. "Endogeneous Quantal Response Equilibrium for Normal Form Games," LIDAM Discussion Papers CORE 2015053, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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    More about this item

    Keywords

    Social networks; Social learning; Quantal Response Equilibrium (QRE); Experiment; D82; D83; C92;
    All these keywords.

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior

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