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Trust, influence, and convergence of behavior in social networks

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  • Pan, Zhengzheng
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    Abstract

    I propose a social learning framework where agents repeatedly take the weighted average of all agents' current opinions in forming their own for the next period. They also update the influence weights that they place on each other. It is proven that both opinions and the influence weights are convergent. In the steady state, opinions reach consensus and influence weights are distributed evenly. Convergence occurs with an extended model as well, which indicates the tremendous influential power possessed by a minority group. Computer simulations of the updating processes provide supportive evidence.

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    File URL: http://www.sciencedirect.com/science/article/B6V88-4YT054B-2/2/30d4069c9e05f8da55f6e6afa5bb854e
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    Bibliographic Info

    Article provided by Elsevier in its journal Mathematical Social Sciences.

    Volume (Year): 60 (2010)
    Issue (Month): 1 (July)
    Pages: 69-78

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    Handle: RePEc:eee:matsoc:v:60:y:2010:i:1:p:69-78

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    Web page: http://www.elsevier.com/locate/inca/505565

    Related research

    Keywords: Social networks Learning Consensus Simulation;

    References

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    1. Ellison, Glenn & Fudenberg, Drew, 1992. "Rules of Thumb for Social Learning," IDEI Working Papers 17, Institut d'Économie Industrielle (IDEI), Toulouse.
    2. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 2.
    3. Ellison, Glenn & Fudenberg, Drew, 1995. "Word-of-Mouth Communication and Social Learning," The Quarterly Journal of Economics, MIT Press, vol. 110(1), pages 93-125, February.
    4. Durieu, Jacques & Solal, Philippe, 2003. "Adaptive play with spatial sampling," Games and Economic Behavior, Elsevier, vol. 43(2), pages 189-195, May.
    5. Lorenz, Jan, 2005. "A stabilization theorem for dynamics of continuous opinions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 217-223.
    6. Duncan J. Watts & Peter Sheridan Dodds, 2007. "Influentials, Networks, and Public Opinion Formation," Journal of Consumer Research, University of Chicago Press, vol. 34(4), pages 441-458, 05.
    7. Banerjee, Abhijit & Fudenberg, Drew, 2004. "Word-of-mouth learning," Games and Economic Behavior, Elsevier, vol. 46(1), pages 1-22, January.
    8. Shiller, Robert J, 1995. "Conversation, Information, and Herd Behavior," American Economic Review, American Economic Association, vol. 85(2), pages 181-85, May.
    9. Gale, Douglas & Kariv, Shachar, 2003. "Bayesian learning in social networks," Games and Economic Behavior, Elsevier, vol. 45(2), pages 329-346, November.
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    Cited by:
    1. repec:hal:cesptp:halshs-00639677 is not listed on IDEAS
    2. Diamantaras, Dimitrios & Gilles, Robert P., 2011. "Ambiguity, social opinion and the use of common property resources," Journal of Economic Behavior & Organization, Elsevier, vol. 80(1), pages 210-222.
    3. Zhengzheng Pan, 2012. "Opinions and Networks: How Do They Effect Each Other," Computational Economics, Society for Computational Economics, vol. 39(2), pages 157-171, February.
    4. Grabisch, Michel & Rusinowska, Agnieszka, 2013. "A model of influence based on aggregation functions," Mathematical Social Sciences, Elsevier, vol. 66(3), pages 316-330.
    5. Berno Buechel & Tim Hellmann & Stefan Kölßner, 2014. "Opinion Dynamics and Wisdom under Conformity," Working Papers 2014.51, Fondazione Eni Enrico Mattei.

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