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An Experimental Study of Persuasion Bias and Social Influence in Networks

Listed author(s):
  • Jordi Brandts

    (Institutd'AnalisiEconomica(CSIC)
    Barcelona GSE)

  • Ayça Ebru Giritligil

    (Murat Sertel Center for Advanced Economic Studies
    İstanbul Bilgi University)

  • Roberto A. Weber

    (Department of Economics, University of Zurich)

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 a number of agents all want to find out the value of a particular parameter unknown to all. Agents receive private signals about the parameter and agents 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 four 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 it optimally should. Our results are consistent with the model of “persuasion bias” of De Marzo et al. (2003) and at odds with an alternative heuristic according to which the most influential agents are those with more incoming links.

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File URL: http://repeck.bilgi.edu.tr/RePEc/beb/wpbels/BelisWP_BELIS03.pdf
File Function: First version, 2014
Download Restriction: no

Paper provided by BELIS, Istanbul Bilgi University in its series BELIS Working Papers with number 2014-03.

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Length: 32 pages
Date of creation: Oct 2014
Handle: RePEc:beb:wpbels:201403
Contact details of provider: Web page: http://belis.bilgi.edu.tr
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  1. Markus Mobius & Tuan Phan & Adam Szeidl, 2015. "Treasure Hunt: Social Learning in the Field," NBER Working Papers 21014, National Bureau of Economic Research, Inc.
  2. Enke, Benjamin & Zimmermann, Florian, 2013. "Correlation Neglect in Belief Formation," IZA Discussion Papers 7372, Institute for the Study of Labor (IZA).
  3. 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.
  4. 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.
  5. Gale, Douglas & Kariv, Shachar, 2003. "Bayesian learning in social networks," Games and Economic Behavior, Elsevier, vol. 45(2), pages 329-346, November.
  6. 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.
  7. Abhijit Banerjee & Arun G. Chandrasekhar & Esther Duflo & Matthew O. Jackson, 2012. "The Diffusion of Microfinance," NBER Working Papers 17743, National Bureau of Economic Research, Inc.
  8. 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.
  9. 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.
  10. 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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