Export citation

Export citation

Choose format for download:

Download Citation
  • Access by Xinjiang University

Stochastic model of agent interaction with opinion leaders

Andrea Ellero1,*, Giovanni Fasano1,2,†, and Annamaria Sorato1,‡

  • 1Department of Management, Ca’ Foscari University of Venice, Venice, Italy
  • 2INSEAN-CNR Italian Ship Model Basin, Rome, Italy

  • *ellero@unive.it
  • fasano@unive.it
  • amsorato@unive.it

Phys. Rev. E 87, 042806 – Published 5 April, 2013

DOI: https://doi.org/10.1103/PhysRevE.87.042806

Abstract

We analyze the problem of agents' interactions in a given population. The purpose of this paper is twofold. Starting from a scheme proposed by Galam [Physica A 320, 571 (2003)], which is based on a majority rule to treat the individuals’ interactions, we first study some of its relevant properties. Then, we introduce special individuals, called opinion leaders, who play a key role in information spreading in several practical applications. Opinion leaders have the special feature of strongly interfering with the process based on the majority rule, speeding up the diffusion. We consider a model describing agents’ interactions, which encompasses Galam's proposal, where opinion leaders are included as special agents. Then we study its specific properties which significantly recast and extend some conclusions drawn for the models given by Galam and Ellero, Fasano, and Sorato [Physica A 388, 3901 (2009)]. Finally, we provide theoretical and numerical results concerning the dynamics of our model, showing that a small percentage of opinion leaders may both accelerate and/or even reverse the overall consensus among all the agents.

Article Text

References (23)

  1. F. M. Bass, Manage. Sci. 15, 215 (1969).
  2. G. Moore, Crossing the Chasm (HarperCollins, New York, 1991).
  3. E. M. Rogers, Diffusion of Innovations, 5th ed. (Free Press, New York, 2003).
  4. T. Garber, J. Goldenberg, B. Libai, and E. Muller, Market. Sci. 23, 419 (2004).
  5. T. C. Schelling, J. Math. Sociol. 1, 143 (1971).
  6. S. Galam, Physica A 320, 571 (2003).
  7. S. Galam, Int. J. Mod. Phys. C 19, 409 (2008).
  8. E. Katz and P. F. Lazarsfeld, Personal Influence: The Part Played by People in the Flow of Mass Communication (Free Press, Glencoe, IL, 1955).
  9. Y. Cho, J. Hwang, and D. Lee, Technol. Forecast. Social Change 79, 97 (2012).
  10. J. Goldenberg, S. Han, D. Lehmann, and J. Hong, J. Market. 73, 1 (2009).
  11. R. Iyengar, C. Van den Bulte, and T. Valente, Market. Sci. 30, 195 (2011).
  12. P. S. van Eck, W. Jager, and P. S. H. Leeflang, J. Prod. Innovat. Manage. 28, 187 (2011).
  13. D. J. Watts and P. S. Dodds, J. Consum. Res. 34, 441 (2007).
  14. E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts, in Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, Hong Kong, China (ACM New York, NY, 2011), pp. 65–74.
  15. D. Brown and N. Hayes, Influencer Marketing: Who Really Influences Your Customers? (Butterworth-Heinemann, Oxford, 2008).
  16. R. Moynihan, Br. Med. J. 336, 1402 (2008).
  17. J. W. Kingdon, Public Opin. Q. 34, 256 (1970).
  18. S. Galam, Physica A 390, 3036 (2011).
  19. T. Bouzdine-Chameeva and S. Galam, Adv. Complex Sys. 14, 871 (2011).
  20. A. Ellero, G. Fasano, and A. Sorato, Physica A 388, 3901 (2009).
  21. A. Papoulis, Probability, Random Variables, and Stochastic Processes, 2nd ed. (McGraw-Hill, New York, 1984).
  22. S. Galam and F. Jacobs, Physica A 381, 366 (2007).
  23. S. Gekle, L. Peliti, and S. Galam, Eur. Phys. J. B 45, 569 (2005).

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation