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Persuasion dynamics

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  • Weisbuch, Gérard
  • Deffuant, Guillaume
  • Amblard, Frédéric

Abstract

We here discuss a model of continuous opinion dynamics in which agents adjust continuous opinions as a result of random binary encounters whenever their difference in opinion is below a given threshold. We concentrate on the version of the model in the presence of few extremists which might drive the dynamics to generalized extremism. A network version of the dynamics is presented here, and its results are compared to those previously obtained for the full-mixing case. The same dynamical regimes are observed, but in rather different parameter regions. We here show that the combination of meso-scale features resulting from the first interaction steps determines the asymptotic state of the dynamics.

Suggested Citation

  • Weisbuch, Gérard & Deffuant, Guillaume & Amblard, Frédéric, 2005. "Persuasion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 555-575.
    • Handle: RePEc:eee:phsmap:v:353:y:2005:i:c:p:555-575
    DOI: 10.1016/j.physa.2005.01.054
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    1. Amblard, Frédéric & Deffuant, Guillaume, 2004. "The role of network topology on extremism propagation with the relative agreement opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 343(C), pages 725-738.
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    Cited by:

    1. Bertotti, Maria Letizia & Modanese, Giovanni, 2011. "From microscopic taxation and redistribution models to macroscopic income distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3782-3793.
    2. Hendrickx, Julien M., 2008. "Order preservation in a generalized version of Krause’s opinion dynamics model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5255-5262.
    3. Gabbay, Michael, 2007. "The effects of nonlinear interactions and network structure in small group opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(1), pages 118-126.
    4. Liang, Haili & Yang, Yiping & Wang, Xiaofan, 2013. "Opinion dynamics in networks with heterogeneous confidence and influence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2248-2256.
    5. Laurent Salzarulo, 2006. "A Continuous Opinion Dynamics Model Based on the Principle of Meta-Contrast," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(1), pages 1-13.
    6. Christopher Watts & Nigel Gilbert, 2014. "Simulating Innovation," Books, Edward Elgar Publishing, number 13981.
    7. Griffin, Christopher & Squicciarini, Anna & Jia, Feiran, 2022. "Consensus in complex networks with noisy agents and peer pressure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    8. Kułakowski, Krzysztof, 2009. "Opinion polarization in the Receipt–Accept–Sample model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(4), pages 469-476.
    9. Li, Mingwu & Dankowicz, Harry, 2019. "Impact of temporal network structures on the speed of consensus formation in opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1355-1370.
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    14. Prettejohn, Brenton J. & Berryman, Matthew J. & McDonnell, Mark D., 2013. "A model of the effects of authority on consensus formation in adaptive networks: Impact on network topology and robustness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 857-868.
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    18. Pedraza, Lucía & Pinasco, Juan Pablo & Saintier, Nicolas & Balenzuela, Pablo, 2021. "An analytical formulation for multidimensional continuous opinion models," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).

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