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Opinion Formation

  • Dirk Helbing
Chapter
Part of the Understanding Complex Systems book series (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.

Keywords

Social Influence Similar Opinion Opinion Distribution Adaptive Noise Interregional Migration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dirk Helbing
    • 1
  1. 1.CLU E1ETH ZurichZurichSwitzerland

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