Advertisement

Multiagent Context-Dependent Model of Opinion Dynamics in a Virtual Society

  • Ivan Derevitskii
  • Oksana Severiukhina
  • Klavdiya Bochenina
  • Daniil Voloshin
  • Anastasia Lantseva
  • Alexander Boukhanovsky
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10861)

Abstract

To describe the diversity of opinions and dynamics of their changes in a society, there exist different approaches—from macroscopic laws of political processes to individual-based cognition and perception models. In this paper, we propose mesoscopic individual-based model of opinion dynamics which tackles the role of context by considering influence of different sources of information during life cycle of agents. The model combines several sub-models such as model of generation and broadcasting of messages by mass media, model of daily activity, contact model based on multiplex network and model of information processing. To show the applicability of the approach, we present two scenarios illustrating the effect of the conflicting strategies of informational influence on a population and polarization of opinions about topical subject.

Keywords

Context-dependent modeling Multiagent modeling Opinion dynamics Virtual society 

Notes

Acknowledgments

This research was supported by The Russian Scientific Foundation, Agreement #14-21-00137-П (02.05.2017).

References

  1. 1.
    Gatti, M., Cavalin, P., Neto, S.B., Pinhanez, C., dos Santos, C., Gribel, D., Appel, A.P.: Large-scale multi-agent-based modeling and simulation of microblogging-based online social network. In: Alam, S.J., Parunak, H. (eds.) MABS 2013. LNCS, vol. 8235, pp. 17–33. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-642-54783-6_2CrossRefGoogle Scholar
  2. 2.
    Ryczko, K., Domurad, A., Buhagiar, N., Tamblyn, I.: Hashkat: large-scale simulations of online social networks. Soc. Netw. Anal. Min. 7, 4 (2017)CrossRefGoogle Scholar
  3. 3.
    Peng, W., Shuang, Y., Jingjing, Z., Qingning, G.: Agent-based modeling and simulation of evolution of netizen crowd behavior in unexpected events public opinion. Data Anal. Knowl. Discov. 31, 65–72 (2015)Google Scholar
  4. 4.
    Zhang, Y., Tanniru, M.: An agent-based approach to study virtual learning communities. In: Proceedings of the 38th Annual Hawaii International Conference on System Sciences, HICSS 2005, p. 11c (2005)Google Scholar
  5. 5.
    Edmonds, B.: The room around the elephant: tackling context-dependency in the social sciences. In: Johnson, J., Nowak, A., Ormerod, P., Rosewell, B., Zhang, Y.-C. (eds.) Non-Equilibrium Social Science and Policy. UCS, pp. 195–208. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-42424-8_13CrossRefGoogle Scholar
  6. 6.
    Hu, H.-B., Wang, X.-F.: Discrete opinion dynamics on networks based on social influence. J. Phys. A Math. Theoret. 42, 225005 (2009).  https://doi.org/10.1088/1751-8113/42/22/225005MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Yildiz, E., Acemoglu, D., Ozdaglar, A., Saberi, A., Scaglione, A.: Discrete Opinion Dynamics with Stubborn Agents*Google Scholar
  8. 8.
    Lorenz, J.: Continuous opinion dynamics under bounded confidence: a survey. Int. J. Mod. Phys. C 18, 1819–1838 (2007)CrossRefGoogle Scholar
  9. 9.
    Martins, A.C.R.: Bayesian updating rules in continuous opinion dynamics models. J. Stat. Mech.: Theory Exp. 2009, P02017 (2009)CrossRefGoogle Scholar
  10. 10.
    Salzarulo, L.: A continuous opinion dynamics model based on the principle of meta-contrast. J. Artif. Soc. Soc. Simul. 9 (2006)Google Scholar
  11. 11.
    Jager, W., Amblard, F.: Uniformity, bipolarization and pluriformity captured as generic stylized behavior with an agent-based simulation model of attitude change. Comput. Math. Organ. Theory 10, 295–303 (2005)CrossRefGoogle Scholar
  12. 12.
    Yu, Y., Xiao, G., Li, G., Tay, W.P., Teoh, H.F.: Opinion diversity and community formation in adaptive networks. Chaos Interdisc. J. Nonlinear Sci. 27, 103115 (2017)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Amblard, F., Deffuant, G.: The role of network topology on extremism propagation with the relative agreement opinion dynamics. Phys. A Stat. Mech. Appl. 343, 725–738 (2004)CrossRefGoogle Scholar
  14. 14.
    Grabowski, A., Kosiński, R.A.: Ising-based model of opinion formation in a complex network of interpersonal interactions. Phys. A Stat. Mech. Appl. 361, 651–664 (2006)CrossRefGoogle Scholar
  15. 15.
    Benczik, I.J., Benczik, S.Z., Schmittmann, B., Zia, R.K.P.: Opinion dynamics on an adaptive random network. Phys. Rev. E 79, 46104 (2009)CrossRefGoogle Scholar
  16. 16.
    Leifeld, P.: Polarization of coalitions in an agent-based model of political discourse. Comput. Soc. Netw. 1, 7 (2014)CrossRefGoogle Scholar
  17. 17.
    Sobkowicz, P.: Opinion dynamics model based on cognitive biases. arXiv Preprint arXiv1703.01501 (2017)
  18. 18.
    Mossong, J., Hens, N., Jit, M., Beutels, P., Auranen, K., Mikolajczyk, R., Massari, M., Salmaso, S., Tomba, G.S., Wallinga, J., et al.: Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Med. 5, e74 (2008)CrossRefGoogle Scholar
  19. 19.
    Graham, J., Haidt, J., Nosek, B.A.: Liberals and conservatives rely on different sets of moral foundations. J. Pers. Soc. Psychol. 96, 1029 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ivan Derevitskii
    • 1
  • Oksana Severiukhina
    • 1
  • Klavdiya Bochenina
    • 1
  • Daniil Voloshin
    • 1
  • Anastasia Lantseva
    • 1
  • Alexander Boukhanovsky
    • 1
  1. 1.ITMO UniversitySaint PetersburgRussia

Personalised recommendations