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Anonymous social influence

  • Förster, Manuel
  • Grabisch, Michel
  • Rusinowska, Agnieszka

We study a stochastic model of influence where agents have “yes” or “no” inclinations on some issue, and opinions may change due to mutual influence among the agents. Each agent independently aggregates the opinions of the other agents and possibly herself. We study influence processes modeled by ordered weighted averaging operators, which are anonymous: they only depend on how many agents share an opinion. For instance, this allows to study situations where the influence process is based on majorities, which are not covered by the classical approach of weighted averaging aggregation. We find a necessary and sufficient condition for convergence to consensus and characterize outcomes where the society ends up polarized. Our results can also be used to understand more general situations, where ordered weighted averages are only used to some extent. Furthermore, we apply our results to fuzzy linguistic quantifiers, i.e., expressions like “most” or “at least a few”.

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Article provided by Elsevier in its journal Games and Economic Behavior.

Volume (Year): 82 (2013)
Issue (Month): C ()
Pages: 621-635

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Handle: RePEc:eee:gamebe:v:82:y:2013:i:c:p:621-635
Contact details of provider: Web page: http://www.elsevier.com/locate/inca/622836

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  1. FÖRSTER, Manuel & GRABISCH, Michel & RUSINOWSKA, Agnieszka, 2013. "Anonymous social influence," CORE Discussion Papers 2013028, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Glen Ellison, 2010. "Learning, Local Interaction, and Coordination," Levine's Working Paper Archive 391, David K. Levine.
  3. Michel Grabisch & Agnieszka Rusinowska, 2010. "A model of influence in a social network," Theory and Decision, Springer, vol. 69(1), pages 69-96, July.
  4. Zwiebel, Jeffrey H. & Vayanos, Dimitri & DeMarzo, Peter M., 2001. "Persuasion Bias, Social Influence, and Uni-Dimensional Opinions," Research Papers 1719, Stanford University, Graduate School of Business.
  5. Michel Grabisch & Agnieszka Rusinowska, 2008. "Influence functions, followers and command games," Documents de travail du Centre d'Economie de la Sorbonne b08080, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
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  7. Abhijit Banerjee & Drew Fudenberg, 2010. "Word of Mouth Learning," Levine's Working Paper Archive 723, David K. Levine.
  8. Sanjeev Goyal & Andrea Galeotti, 2007. "A Theory of Strategic Diffusion," Working Papers 2007.70, Fondazione Eni Enrico Mattei.
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  16. Borm, P.E.M. & van den Brink, J.R. & Slikker, M., 2002. "An iterative procedure for evaluating digraph competitions," Other publications TiSEM 40ae2ec2-efdb-48f6-905c-5, Tilburg University, School of Economics and Management.
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  18. Michel Grabisch & Agnieszka Rusinowska, 2011. "A model of influence based on aggregation functions," Documents de travail du Centre d'Economie de la Sorbonne 11058, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  19. Bala, V. & Goyal, S., 1995. "Learning from Neighbors," Econometric Institute Research Papers EI 9549-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  20. Gale, Douglas & Kariv, Shachar, 2003. "Bayesian learning in social networks," Games and Economic Behavior, Elsevier, vol. 45(2), pages 329-346, November.
  21. Manuel Förster & Michel Grabisch & Agnieszka Rusinowska, 2012. "Ordered Weighted Averaging in Social Networks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00746988, HAL.
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  23. Hu, Xingwei & Shapley, Lloyd S., 2003. "On authority distributions in organizations: equilibrium," Games and Economic Behavior, Elsevier, vol. 45(1), pages 132-152, October.
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