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Research of Opinion Dynamic Evolution Based on Flocking Theory

  • Shan Liu
  • Rui Tang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)

Abstract

Using natural science research methods to study the behavior and phenomenon in complex social groups has attracted great concern in recent years. The opinion refers to the views, choices, or preferences that individual have in one thing. The main study of opinion dynamics is the evolution process of individual views from disorder to order in social groups. Under the background of flocking theory, we proposed an individual opinion impact model based on Agent. We also analyzed the evolution of ideas and emergence of cluster. The effectiveness of proposed model is validated with simulation on the impacts of the opinion’s formation and evolution. The simulation results illustrate an effective interpretation of some phenomena in reality.

Keywords

Flocking theory Opinion dynamics Continuous opinion Opinion evolution 

References

  1. 1.
    Xia, H., Wang, H., Xuan, Z.: Opinion dynamics: a multidisciplinary review and perspective on future research. IGI Global (2011)Google Scholar
  2. 2.
    Jiongming, S.: Continous opinion dynamics evolution on social networks and its application in online prediction. National University of Defense Technology (2014)Google Scholar
  3. 3.
    Weidlich, W.: The statistical description of polarization phenomena in society †. Br. J. Math. Stat. Psychol. 24(2), 251–266 (1971)CrossRefGoogle Scholar
  4. 4.
    Galam, S., Gefen, Y., Shapir, Y.: Sociophysics: a new approach of sociological collective behaviour: I. Mean-behaviour description of a strike. J. Math. Sociol. 9(1), 1–13 (1982)CrossRefGoogle Scholar
  5. 5.
    Stauffer, D., Penna, T.J.P.: Cross over in the Cont-Bounchaud percolation model for mark fluctuations. Phys. A 256, 284–290 (1998)CrossRefGoogle Scholar
  6. 6.
    Schweitzer, F., Hołyst, J.A.: Modelling collective opinion formation by means of active Brownian particles. Eur. Phys. J. B – Condens. Matter and Complex Syst. 15(4), 723–732 (2000)CrossRefGoogle Scholar
  7. 7.
    Higgs, P.G., Derrida, B.: Stochastic models for species formation in evolving populations. J. Phys. A Gen. Phys. 24(17), L985 (1991)CrossRefGoogle Scholar
  8. 8.
    Florian, R., Galam, S.: Optimizing conflicts in the formation of strategic alliances. Eur. Phys. J. B – Condens. Matter Complex Syst. 16(1), 189–194 (2000)CrossRefGoogle Scholar
  9. 9.
    Bak, P., Tang, C., Wiesenfeld, K.: Self-organized criticality: An explanation of the 1/f noise. Phys. Rev. Lett. 59(4), 381–384 (1987)CrossRefGoogle Scholar
  10. 10.
    Bak, P., Sneppen, K.: Punctuated equilibrium and criticality in a simple model of evolution. Phys. Rev. Lett. 71(24), 4083–4086 (1993)CrossRefGoogle Scholar
  11. 11.
    Pekalski, A.E., Sznajdweron, K.E.: Exotic statistical physics: proceedings of the 36th Karpacz winter school in theoretical physics held in Ladek Zdrój, Poland 11–19 February 2000Google Scholar
  12. 12.
    Bernardes, A.T., Costa, U.M.S., Araujo, A.D., Stauffer, D.: Damage spreading, coarsening dynamics and distribution of political votes in Sznajd model on square lattice. Int. J. Mod. Phys. C 12(02), 159–167 (2001)CrossRefGoogle Scholar
  13. 13.
    Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. In: Conference on Computer Graphics and Interactive Techniques, vol. 21, pp. 25–34. ACM (1987)Google Scholar
  14. 14.
    Lewenstein, M., Nowak, A., Latané, B.: Statistical mechanics of social impact. Phys. Rev. A At., Mol., Opt. Phys. 45(2), 763 (1992)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Information Engineering SchoolCommunication University of ChinaBeijingChina

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