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An Experimental Study of Persuasion Bias and Social Influence in Networks

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  • Jordi Brandts
  • Ayça Ebru
  • Roberto A. Weber

Abstract

In many areas of social life, individuals receive information about a particular issue of interest from multiple sources. When these sources are connected through a network, then proper aggregation of this information by an individual involves taking into account the structure of this network. The inability to aggregate properly may lead to various types of distortions. In our experiment, four agents all want to find out the value of a particular parameter unknown to all. Agents receive private signals about the parameter and can communicate their estimates of the parameter repeatedly through a network, the structure of which is known by all players. We present results from experiments with three different networks. We find that the information of agents who have more outgoing links in a network gets more weight in the information aggregation of the other agents than under optimal updating. Our results are consistent with the model of "persuasion bias" of DeMarzo et al. (2003).

Suggested Citation

  • Jordi Brandts & Ayça Ebru & Roberto A. Weber, 2015. "An Experimental Study of Persuasion Bias and Social Influence in Networks," Working Papers 829, Barcelona School of Economics.
    • Handle: RePEc:bge:wpaper:829
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    References listed on IDEAS

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    1. Benjamin Enke & Florian Zimmermann, 2019. "Correlation Neglect in Belief Formation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(1), pages 313-332.
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    7. Peter M. DeMarzo & Dimitri Vayanos & Jeffrey Zwiebel, 2003. "Persuasion Bias, Social Influence, and Unidimensional Opinions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 909-968.
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    9. Pietro Battiston & Luca Stanca, 2014. "Boundedly Rational Opinion Dynamics in Directed Social Networks: Theory and Experimental Evidence," Working Papers 267, University of Milano-Bicocca, Department of Economics, revised Jan 2014.
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    Cited by:

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    2. Szeidl, Adam & Mobius, Markus & Phan, Tuan, 2015. "Treasure Hunt: Social Learning in the Field," CEPR Discussion Papers 10493, C.E.P.R. Discussion Papers.
    3. Berno Buechel & Stefan Klößner & Martin Lochmüller & Heiko Rauhut, 2020. "The strength of weak leaders: an experiment on social influence and social learning in teams," Experimental Economics, Springer;Economic Science Association, vol. 23(2), pages 259-293, June.
    4. Rapanos, Theodoros, 2023. "What makes an opinion leader: Expertise vs popularity," Games and Economic Behavior, Elsevier, vol. 138(C), pages 355-372.
    5. Jakob Grazzini & Domenico Massaro, 2016. "Dispersed Information and the Origins of Aggregate Fluctuations," CESifo Working Paper Series 5957, CESifo.
    6. Isabel Melguizo, 2019. "Homophily and the Persistence of Disagreement," The Economic Journal, Royal Economic Society, vol. 129(619), pages 1400-1424.
    7. João V. Ferreira & Erik Schokkaert & Benoît Tarroux, 2023. "How group deliberation affects individual distributional preferences: An experimental study," Working Papers 2301, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    8. Battiston, Pietro & Stanca, Luca, 2015. "Boundedly rational opinion dynamics in social networks: Does indegree matter?," Journal of Economic Behavior & Organization, Elsevier, vol. 119(C), pages 400-421.
    9. Foerster, Manuel, 2018. "Finite languages, persuasion bias, and opinion fluctuations," Journal of Economic Behavior & Organization, Elsevier, vol. 149(C), pages 46-57.
    10. Mohsen Foroughifar, 2021. "Errors in Learning from Others' Choices," Papers 2105.01043, arXiv.org, revised Aug 2021.
    11. Friederike Mengel, 2021. "Gender Bias In Opinion Aggregation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(3), pages 1055-1080, August.
    12. Naegels, Vanessa & D’Espallier, Bert & Mori, Neema, 2020. "Perceived problems with collateral: The value of informal networking," International Review of Economics & Finance, Elsevier, vol. 65(C), pages 32-45.

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    More about this item

    Keywords

    persuasion bias; experiments; bounded rationality;
    All these keywords.

    JEL classification:

    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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