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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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    References listed on IDEAS

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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. 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.
    2. 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.
    3. 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.
    4. Maciel, Marcelo V. & Martins, André C.R., 2020. "Ideologically motivated biases in a multiple issues opinion model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    5. 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.
    6. Martins, André C.R., 2022. "Extremism definitions in opinion dynamics models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    7. 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.
    8. 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.
    9. Chen, Shuwei & Glass, David H. & McCartney, Mark, 2016. "Characteristics of successful opinion leaders in a bounded confidence model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 426-436.
    10. Galesic, Mirta & Stein, D.L., 2019. "Statistical physics models of belief dynamics: Theory and empirical tests," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 275-294.
    11. 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.
    12. Weimer-Jehle, Wolfgang, 2008. "Cross-impact balances," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3689-3700.
    13. Henkel, Christof, 2017. "From quantum mechanics to finance: Microfoundations for jumps, spikes and high volatility phases in diffusion price processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 447-458.
    14. 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.
    15. Christopher Watts & Nigel Gilbert, 2014. "Simulating Innovation," Books, Edward Elgar Publishing, number 13981.
    16. 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).
    17. Takesue, Hirofumi, 2023. "Relative opinion similarity leads to the emergence of large clusters in opinion formation models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    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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