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Opinion Formation

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Social Self-Organization

Part of the book series: Understanding Complex Systems ((UCS))

Abstract

One of the most intriguing dynamics in biological systems is the emergence of clustering, in the sense that individuals self-organize into separate agglomerations in physical or behavioral space. Several theories have been developed to explain clustering in, for instance, multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of fish, and animal herds. A persisting puzzle, however is the clustering of opinions in human populations, particularly when opinions vary continuously, such as the degree to which citizens are in favor of or against a vaccination program. Existing continuous opinion formation models predict “monoculture” in the long run, unless subsets of the population are perfectly separated from each other. Yet, social diversity is a robust empirical phenomenon, although perfect separation is hardly possible in an increasingly connected world. Considering randomness did not overcome the theoretical shortcomings so far. Small perturbations of individual opinions trigger social influence cascades that inevitably lead to monoculture, while larger noise disrupts opinion clusters and results in rampant individualism without any social structure. Our solution of the puzzle builds on recent empirical research, combining the integrative tendencies of social influence with the disintegrative effects of individualization. A key element of the new computational model is an adaptive kind of noise. We conduct computer simulation experiments demonstrating that with this kind of noise a third phase besides individualism and monoculture becomes possible, characterized by the formation of metastable clusters with diversity between and consensus within clusters. When clusters are small, individualization tendencies are too weak to prohibit a fusion of clusters. When clusters grow too large, however, individualization increases in strength, which promotes their splitting. In summary, the new model can explain cultural clustering in human societies. Strikingly, model predictions are not only robust to “noise” – randomness is actually the central mechanism that sustains pluralism and clustering.

This chapter reprints a previously published paper and should be cited as follows: M. Mäs, A. Flache, and D. Helbing (2010) Individualization as driving force of clustering phenomena in humans. PLoS Comput. Biol. 6(10), e1000959.

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References

  1. E. Ben-Jacob, O. Schochet, A. Tenenbaum, I. Cohen, A. Czirok, T. Vicsek, Generic Modeling of Cooperative Growth-Patterns in Bacterial Colonies. Nature 368, 46–49 (1994)

    Google Scholar 

  2. R. Jeanson, C. Rivault, J.L. Deneubourg, S. Blanco, R. Fournier, C. Jost, G. Theraulaz, Self-organized aggregation in cockroaches. Anim. Behav. 69, 169–180 (2005)

    Google Scholar 

  3. J. Gautrais, C. Jost, G. Theraulaz, Key behavioural factors in a self-organised fish school model. Ann. Zool. Fenn. 45, 415–428 (2008)

    Google Scholar 

  4. M. Ballerini, N. Calbibbo, R. Candeleir, A. Cavagna, E. Cisbani, et al., Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proc. Natl. Acad. Sci. USA 105, 1232–1237 (2008)

    Google Scholar 

  5. I.D. Couzin, J. Krause, N.R. Franks, S.A. Levin, Effective leadership and decision-making in animal groups on the move. Nature 433, 513–516 (2005)

    Google Scholar 

  6. Y. Iwasa, V. Andreasen, S.A. Levin, Aggregation in model ecosystems. 1. Perfect Aggregation. Ecol. Model 37, 287–302 (1987)

    Google Scholar 

  7. M. Moussaid, N. Perozo, S. Garnier, D. Helbing, G. Theraulaz, The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PLoS One 5(4), e10047 (2010)

    Google Scholar 

  8. H.A. Makse, S. Havlin, H.E. Stanley, Modeling Urban-Growth Patterns. Nature 377, 608–612 (1995)

    Google Scholar 

  9. M. Batty, The size, scale, and shape of cities. Science 319, 769–771 (2008)

    Google Scholar 

  10. D.J. Watts, S.H. Strogatz, Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)

    Google Scholar 

  11. A.L. Barabasi, R. Albert, Emergence of scaling in random networks. Science 286, 509–512 (1999)

    Google Scholar 

  12. G. Palla, A.L. Barabasi, T. Vicsek, Quantifying social group evolution. Nature 446, 664–667 (2007)

    Google Scholar 

  13. D. Liben-Nowell, J. Kleinberg, Tracing information flow on a global scale using Internet chain-letter data. Proc. Natl. Acad. Sci. USA 105, 4633–4638 (2008)

    Google Scholar 

  14. M.E.J. Newman, Coauthorship networks and patterns of scientific collaboration. Proc. Natl. Acad. Sci. USA 101, 5200–5205 (2004)

    Google Scholar 

  15. F. Liljeros, C.R. Edling, L.A.N. Amaral, H.E. Stanley, Y. Aberg, The web of human sexual contacts. Nature 411, 907–908 (2001)

    Google Scholar 

  16. M.P. Fiorina, S.J. Abrams, Political polarization in the American public. Annu. Rev. Polit. Sci. 11, 563–588 (2008)

    Google Scholar 

  17. P. DiMaggio, J. Evans, B. Bryson, Have Americans’ social attitudes become more polarized? Am. J. Sociol. 102, 690–755 (1996)

    Google Scholar 

  18. E.L. Glaeser, B.A. Ward, Myths and realities of American political geography. J. Econ. Perspect. 20, 119–144 (2006)

    Google Scholar 

  19. N.P. Mark, Culture and competition: Homophily and distancing explanations for cultural niches. Am. Sociol. Rev. 68, 319–345 (2003)

    Google Scholar 

  20. D. Lazer, et al., Computational Social Science. Science 323, 721–723 (2009)

    Google Scholar 

  21. F.J. Milliken, L.L. Martins, Searching for Common Threads: Understanding the Multiple Effects of Diversity in Organizational Groups. Acad. Manag. J. 21, 402–433 (1996)

    Google Scholar 

  22. P.C. Early, E. Mosakowski, Creating hybrid team cultures: an empirical test of transnational team functioning. Acad. Manag. J. 43, 26–49 (2000)

    Google Scholar 

  23. T.L. Friedman, The World is Flat. A brief history of the twenty-first century (Farrar, Straus and Giroux, New York, 2005)

    Google Scholar 

  24. M.J. Greig, The end of geography? Globalization, communication and culture in the international system. J. Conflict Res. 46, 225–243 (2002)

    Google Scholar 

  25. W.J. Sutherland, Parallel extinction risk and global distribution of languages and species. Nature 423, 276–279 (2003)

    Google Scholar 

  26. E. Durkheim, The Division of Labor in Society (The Free Press, New York, 1997 [1893])

    Google Scholar 

  27. U. Beck, in Reflexive Modernization. Politics, Tradition and Aestherics in the Modern Social Order, ed. by U. Beck, A. Giddens, S. Lash (Polity Press, Cambridge, 1994)

    Google Scholar 

  28. M. McPherson, L. Smith-Lovin, M.E. Brashears, Social isolation in America: Changes in core discussion networks over two decades. Am. Sociol. Rev. 71, 353–375; also consult the discussion on this article in Am. Sociol. Rev. 74, 4 (2006)

    Google Scholar 

  29. R.P. Abelson, in Contributions to Mathematical Psychology, ed. by N. Frederiksen, H. Gulliksen (Rinehart Winston, New York, 1964), pp. 142–160

    Google Scholar 

  30. M. McPherson, L. Smith-Lovin, J.M. Cook, Birds of a feather: Homophily in social networks. Annu. Rev. Sociol. 27, 415–444 (2001)

    Google Scholar 

  31. S. Aral, L. Muchnik, A. Sundararajan, Distinguishing influenced-based contagion from homophily-driven diffusion in dynamic networks. Proc. Natl. Acad. Sci. USA 106, 21544–21549 (2009)

    Google Scholar 

  32. A. Nowak, J. Szamrej, B. Latané, From private attitude to public opinion: a dynamic theory of social impact. Psychol. Rev. 97, 362–376 (1990)

    Google Scholar 

  33. R. Hegselmann, U. Krause, Opinion dynamics and bounded confidence models. Analysis, and Simulation J. Artif. Soc. S 5 (2002)

    Google Scholar 

  34. G. Deffuant, S. Huet, F. Amblard, An individual-based model of innnovation diffusion mixing social value and individual benefit. Am. J. Soc. 110, 1041–1069 (2005)

    Google Scholar 

  35. R. Axelrod, The dissemination of culture - A model with local convergence and global polarization. J. Conflict Res. 41, 203–226 (1997)

    Google Scholar 

  36. M.W. Macy, J. Kitts, A. Flache, S. Benard, in Dynamic Social Network Modelling and Analysis, ed. by R. Breiger, K. Carley, P. Pattison (The National Academies Press, Washington, DC, 2003), pp. 162–173

    Google Scholar 

  37. A. Flache, M. Mäs, How to get the timing right? A computational model of how demographic faultlines undermine team performance and how the right timing of contacts can solve the problem. Comput. Math. Organ. Theory 14, 23–51 (2008)

    Google Scholar 

  38. Z. Krizan, R.S. Baron, Group polarization and choice-dilemmas: How important is self-categorization? Eur. J. Soc. Psychol. 37, 191–201 (2007)

    Google Scholar 

  39. M. Pineda, R. Toral, E. Hernandez-Garcia, Noisy continuous-opinion dynamics. J. Stat. Mech. P08001 (2009)

    Google Scholar 

  40. M.J. Hornsey, J. Jetten, The individual within the group: Balancing the need to belong with the need to be different. Pers. Soc. Psychol. Rev. 8, 248–264 (2004)

    Google Scholar 

  41. V.L. Vignoles, X. Chryssochoou, G.M. Breakwell, The distinctiveness principle: Identity, meaning, and the bounds of cultural relativity. Pers. Soc. Psychol. Rev. 4, 337–354 (2000)

    Google Scholar 

  42. R. Imhoff, H.P. Erb, What motivates nonconformity? Uniqueness seeking blocks majority influence. Pers. Soc. Psychol. B 35, 309–320 (2009)

    Google Scholar 

  43. C.R. Snyder, H.L. Fromkin, Uniqueness. The Human Pursuit of Difference (Plenum Press, New York and London, 1980)

    Google Scholar 

  44. K. Klemm, V.M. Eguiluz, R. Toral, M.S. Miguel, Global culture: A noise-induced transition in finite systems. Phys. Rev. E 67, 045101(R) (2003)

    Google Scholar 

  45. H.E. Stanley, Introduction to Phase Transitions and Critical Phenomena (Oxford University Press, Oxford and New York, 1971)

    Google Scholar 

  46. W. Weidlich, The statistical description of polarization phenomena in society. Br. J. Math. Stat. Psychol. 24, 251–266 (1971)

    Google Scholar 

  47. T.M. Liggett, Interacting Particle Systems (Springer, New York, 1985)

    Google Scholar 

  48. K. Sznajd-Weron, J. Sznajd, Opinion Evolution in Closed Community. Int. J. Mod. Phys. C 11, 1157–1165 (2000)

    Google Scholar 

  49. S. Galam, Heterogeneous beliefs, segregation, and extremism in the making of public opinions. Phys. Rev. E 71, 46123 (2005)

    Google Scholar 

  50. L. Behera, F. Schweitzer, On spatial consensus formation: Is the Sznajd model different from a voter model? International Journal of Modern Physics C 14(10), 1331–1354 (2003)

    Google Scholar 

  51. E. Bonabeau, Agent-based modeling: Methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. USA 99, 7280–7287 (2002)

    Google Scholar 

  52. E. Durkheim, Suicide. A study in Sociology (The Free Press, New York, 1997 [1897])

    Google Scholar 

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Acknowledgements

We thank Tobias Stark, Heiko Rauhut, Jacob G. Foster and Michael Macy as well as the members of the Norms and Networks cluster at the Department of Sociology at the University of Groningen and the members of the Cooperative Relations and Social Networks Seminar at the Department of Sociology at Utrecht University for their constructive comments.

Author Contributions Conceived and designed the experiments: MM AF DH. Performed the experiments: MM. Analyzed the data: MM. Wrote the paper: MM AF DH.

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  1. CLU E1, ETH Zurich, Clausiusstrasse 50, 8092, Zurich, Switzerland

    Dirk Helbing (Chair of Sociology, in particular of Modeling and Simulation)

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  1. Lehrstuhl Soziologie, CLU E1, ETH Zürich, Clausiusstr. 50, Zürich, 8092, Switzerland

    Dirk Helbing

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Helbing, D. (2012). Opinion Formation. In: Helbing, D. (eds) Social Self-Organization. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24004-1_4

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  • DOI: https://doi.org/10.1007/978-3-642-24004-1_4

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