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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 Graduate School of Economics.
  • Handle: RePEc:bge:wpaper:829
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    References listed on IDEAS

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    1. Enke, Benjamin & Zimmermann, Florian, 2013. "Correlation Neglect in Belief Formation," IZA Discussion Papers 7372, Institute for the Study of Labor (IZA).
    2. Armin Falk & James J. Heckman, 2009. "Lab Experiments are a Major Source of Knowledge in the Social Sciences," Working Papers 200935, Geary Institute, University College Dublin.
    3. Gale, Douglas & Kariv, Shachar, 2003. "Bayesian learning in social networks," Games and Economic Behavior, Elsevier, vol. 45(2), pages 329-346, November.
    4. 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.
    5. Mobius, Markus & Phan, Tuan & Szeidl, Adam, 2015. "Treasure Hunt: Social Learning in the Field," CEPR Discussion Papers 10493, C.E.P.R. Discussion Papers.
    6. Peter M. DeMarzo & Dimitri Vayanos & Jeffrey Zwiebel, 2003. "Persuasion Bias, Social Influence, and Unidimensional Opinions," The Quarterly Journal of Economics, Oxford University Press, vol. 118(3), pages 909-968.
    7. Banerjee, Abhijit & Chandrasekhar, Arun G & Duflo, Esther & Jackson, Matthew O., 2012. "The Diffusion of Microfinance," CEPR Discussion Papers 8770, C.E.P.R. Discussion Papers.
    8. 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.
    9. Daron Acemoglu & Munther A. Dahleh & Ilan Lobel & Asuman Ozdaglar, 2011. "Bayesian Learning in Social Networks," Review of Economic Studies, Oxford University Press, vol. 78(4), pages 1201-1236.
    10. Enke, Benjamin & Zimmermann, Florian, 2013. "Correlation Neglect in Belief Formation," IZA Discussion Papers 7372, Institute for the Study of Labor (IZA).
    11. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
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    Cited by:

    1. Mobius, Markus & Phan, Tuan & Szeidl, Adam, 2015. "Treasure Hunt: Social Learning in the Field," CEPR Discussion Papers 10493, C.E.P.R. Discussion Papers.
    2. Jakob Grazzini & Domenico Massaro, 2016. "Dispersed Information and the Origins of Aggregate Fluctuations," CESifo Working Paper Series 5957, CESifo Group Munich.
    3. Isabel Melguizo, 2017. "Homophily and the Persistence of Disagreement," Working Paper Series Sobre México 2017001, Sobre México. Temas en economía.

    More about this item

    Keywords

    persuasion bias; experiments; bounded rationality;

    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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