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Opinion Dynamics with Disagreement and Modulated Information

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Abstract

Opinion dynamics concerns social processes through which populations or groups of individuals agree or disagree on specific issues. As such, modelling opinion dynamics represents an important research area that has been progressively acquiring relevance in many different domains. Existing approaches have mostly represented opinions through discrete binary or continuous variables by exploring a whole panoply of cases: e.g. independence, noise, external effects, multiple issues. In most of these cases the crucial ingredient is an attractive dynamics through which similar or similar enough agents get closer. Only rarely the possibility of explicit disagreement has been taken into account (i.e., the possibility for a repulsive interaction among individuals’ opinions), and mostly for discrete or 1-dimensional opinions, through the introduction of additional model parameters. Here we introduce a new model of opinion formation, which focuses on the interplay between the possibility of explicit disagreement, modulated in a self-consistent way by the existing opinions’ overlaps between the interacting individuals, and the effect of external information on the system. Opinions are modelled as a vector of continuous variables related to multiple possible choices for an issue. Information can be modulated to account for promoting multiple possible choices. Numerical results show that extreme information results in segregation and has a limited effect on the population, while milder messages have better success and a cohesion effect. Additionally, the initial condition plays an important role, with the population forming one or multiple clusters based on the initial average similarity between individuals, with a transition point depending on the number of opinion choices.

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References

  1. Acemoglu, D., Como, G.: Opinion fluctuations and disagreement in social networks. Massachusetts Institute of Technology Department of Economics Working Paper Series (2010)

  2. Axelrod, R.: The dissemination of culture: a model with local convergence and global polarization. J. Confl. Resolut. 41(2), 203–226 (1997)

    Article  Google Scholar 

  3. Carletti, T., Fanelli, D., Grolli, S., Guarino, A.: How to make an efficient propaganda. Europhys. Lett. 74(2), 222–228 (2006)

    Article  ADS  Google Scholar 

  4. Carro, A., Toral, R., San Miguel, M.: The role of noise and initial conditions in the asymptotic solution of a bounded confidence, continuous-opinion model. arXiv:1208.2618 (2012)

  5. Castellano, C., Fortunato, S., Loreto, V.: Statistical physics of social dynamics. Rev. Mod. Phys. 81(2), 591 (2009)

    Article  ADS  Google Scholar 

  6. Crokidakis, N.: Effects of mass media on opinion spreading in the Sznajd sociophysics model. Physica A 391, 1729–1734 (2011)

    Article  ADS  Google Scholar 

  7. Deffuant, G., Neau, D., Amblard, F., Weisbuch, G.: Mixing beliefs among interacting agents. Adv. Complex Syst. 3(4), 87–98 (2000)

    Article  Google Scholar 

  8. Deffuant, G., Carletti, T., Huet, S.: The Leviathan model: Absolute dominance. generalised distrust and other patterns emerging from combining vanity with opinion propagation, 12 pp. arXiv:1203.3065v1 (2012)

  9. Degusta, M.: Are smart phones spreading faster than any technology in human history? MIT Technology Review (2012)

  10. Duhigg, C.: The Power of Habit: Why We Do What We Do in Life and Business. Random House, New York (2012)

    Google Scholar 

  11. EveryAware Consortium: EveryAware: enhance environmental awareness through social information technologies. http://www.everyaware.eu

  12. Fortunato, S., Latora, V., Pluchino, A., Rapisarda, A.: Vector opinion dynamics in a bounded confidence consensus model. Int. J. Mod. Phys. C 16(10), 1535–1551 (2005). doi:10.1142/S0129183105008126

    Article  ADS  MATH  Google Scholar 

  13. Galam, S.: Sociophysics: a review of Galam models. Int. J. Mod. Phys. C 19, 403–440 (2008)

    Article  ADS  Google Scholar 

  14. Galam, S.: Public debates driven by incomplete scientific data: the cases of evolution theory, global warming and H1N1 pandemic influenza. Physica A 389(17), 3619–3631 (2010)

    Article  MathSciNet  ADS  Google Scholar 

  15. Galam, S.: Market efficiency. anticipation and the formation of bubbles-crashes, 12 pp. arXiv:1106.1577 (2011)

  16. Gargiulo, F., Lottini, S., Mazzoni, A.: The saturation threshold of public opinion: are aggressive media campaigns always effective? In: ESSA, pp. 1–4 (2008)

  17. González-Avella, J.C., Cosenza, M.G., Eguíluz, V.M., San Miguel, M.: Spontaneous ordering against an external field in non-equilibrium systems. New J. Phys. 12(1), 013010 (2010)

    Article  ADS  Google Scholar 

  18. Hegselmann, R., Krause, U.: Opinion dynamics and bounded confidence: models, analysis and simulation. J. Artif. Soc. Soc. Simul. 5(3), 1–33 (2002)

    Google Scholar 

  19. Hegselmann, R., Krause, U.: Truth and cognitive division of labour: first steps towards a computer aided social epistemology. J. Artif. Soc. Soc. Simul. 9(3), 1–28 (2006)

    Google Scholar 

  20. Hong, H., Strogatz, S.: Conformists and contrarians in a Kuramoto model with identical natural frequencies. Phys. Rev. E 84(4), 1–6 (2011)

    Article  Google Scholar 

  21. Huckfeldt, R., Johnson, P., Sprague, J.: Political Disagreement: The Survival of Diverse Opinions Within Communication Networks. Cambridge University Press, Cambridge (2004)

    Book  Google Scholar 

  22. Kondrat, G., Sznajd-Weron, K.: Spontaneous reorientations in a model of opinion dynamics with anticonformists. Int. J. Mod. Phys. C 21, 559–566 (2010)

    Article  ADS  Google Scholar 

  23. Kurmyshev, E., Juárez, H., González-Silva, R.: Dynamics of bounded confidence opinion in heterogeneous social networks: concord against partial antagonism. Physica A 390(16), 2945–2955 (2011)

    Article  ADS  Google Scholar 

  24. Kurz, S., Rambau, J.: On the Hegselmann-Krause conjecture in opinion dynamics. J. Differ. Equ. Appl. 13(6), 859–876 (2011)

    Article  MathSciNet  Google Scholar 

  25. Laguna, M.F., Abramson, G., Zanette, D.H.: Vector opinion dynamics in a model for social influence. Physica A 329, 459–472 (2003)

    Article  MathSciNet  ADS  MATH  Google Scholar 

  26. Lewenstein, M., Nowak, A., Latané, B.: Statistical mechanics of social impact. Phys. Rev. A 45(2), 763–776 (1992)

    Article  MathSciNet  ADS  Google Scholar 

  27. Lima, F.W.S.: Controlling the tax evasion dynamics via majority-vote model on various topologies. Theor. Econ. Lett. 2, 87–93 (2012)

    Article  Google Scholar 

  28. Lorenz, J.: Continuous opinion dynamics of multidimensional allocation problems under bounded confidence: More dimensions lead to better chances for consensus. Eur. J. Econ. Soc. Syst. 19, 213–227 (2006)

    Google Scholar 

  29. Lorenz, J.: Fostering consensus in multidimensional continuous opinion dynamics under bounded confidence. In: Helbing, D. (ed.) Managing Complexity: Insights, Concepts, Applications, Understanding Complex Systems, vol. 32, pp. 321–334. Springer, Berlin/Heidelberg (2008)

    Chapter  Google Scholar 

  30. Manning, C., Schütze, H.: Foundations of Statistical Natural Language Processing, vol. 999. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  31. Martins, A.: Continuous opinions and discrete actions in opinion dynamics problems. Int. J. Mod. Phys. C 19, 617–624 (2008)

    Article  ADS  MATH  Google Scholar 

  32. Nowak, A., Kuã, M.: Simulating the coordination of individual economic decisions. Physica A 287, 613–630 (2000)

    Article  ADS  Google Scholar 

  33. Nowak, A., Lewenstein, M.: Modeling social change with cellular automata. In: Modelling and Simulation in the Social Sciences from a Philosophy of Science Point of View, pp. 249–285. Kluwer Academic, Dordrecht (1996)

    Chapter  Google Scholar 

  34. Nyczka, P., Sznajd-Weron, K., Cislo, J.: Phase transitions in the q-voter model with two types of stochastic driving. Phys. Rev. E 86(1), 011105 (2012)

    Article  ADS  Google Scholar 

  35. Peres, L., Fontanari, J.: The mass media destabilizes the cultural homogenous regime in Axelrod’s model. Journal of Physics A: Mathematical and Theoretical 43, 055003 (2010)

    Article  MathSciNet  ADS  Google Scholar 

  36. Peres, L., Fontanari, J.: The media effect in Axelrod’s model explained. Europhys. Lett. 96, 38004 (2011)

    Article  ADS  Google Scholar 

  37. Radillo-Díaz, A., Pérez, L.A., del Castillo-Mussot, M.: Axelrod models of social influence with cultural repulsion. Phys. Rev. E 80(6), 1–6 (2009)

    Article  Google Scholar 

  38. Sznajd-Weron, K., Sznajd, J.: Opinion evolution in closed community. Int. J. Mod. Phys. C 11, 1157–1165 (2000)

    Article  ADS  Google Scholar 

  39. Sznajd-Weron, K., Tabiszewski, M., Timpanaro, A.: Phase transition in the Sznajd model with independence. Europhys. Lett. 96(4), 1–6 (2011)

    Article  Google Scholar 

  40. Vallacher, R.R., Nowak, A.: Dynamical social psychology: on complexity and coordination in human experience. In: Uhl-Bien, M., Marion, R. (eds.) Complexity Leadership, vol. 1, Chap. 3, pp. 49–80. Information Age, Charlotte (2008)

    Google Scholar 

  41. Vaz Martins, T., Pineda, M., Toral, R.: Mass media and repulsive interactions in continuous-opinion dynamics. Europhys. Lett. 91(4), 48003 (2010)

    Article  ADS  Google Scholar 

  42. Weisbuch, G., Deffuant, G., Amblard, F., Nadal, J.P.: Meet, discuss, and segregate! Complexity 7(3), 55–63 (2002)

    Article  Google Scholar 

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Acknowledgements

This research has been supported by the EveryAware project funded by the Future and Emerging Technologies program (IST-FET) of the European Commission under the EU RD contract IST-265432 and the EuroUnderstanding Collaborative Research Projects DRUST funded by the European Science Foundation.

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Correspondence to Alina Sîrbu.

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Sîrbu, A., Loreto, V., Servedio, V.D.P. et al. Opinion Dynamics with Disagreement and Modulated Information. J Stat Phys 151, 218–237 (2013). https://doi.org/10.1007/s10955-013-0724-x

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  • DOI: https://doi.org/10.1007/s10955-013-0724-x

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