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Homogeneous Symmetrical Threshold Model with Nonconformity: Independence versus Anticonformity

Author

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  • Bartłomiej Nowak
  • Katarzyna Sznajd-Weron

Abstract

We study two variants of the modified Watts threshold model with a noise (with nonconformity, in the terminology of social psychology) on a complete graph. Within the first version, a noise is introduced via so-called independence, whereas in the second version anticonformity plays the role of a noise, which destroys the order. The modified Watts threshold model, studied here, is homogeneous and possesses an up-down symmetry, which makes it similar to other binary opinion models with a single-flip dynamics, such as the majority-vote and the - voter models. Because within the majority-vote model with independence only continuous phase transitions are observed, whereas within the - voter model with independence also discontinuous phase transitions are possible, we ask the question about the factor, which could be responsible for discontinuity of the order parameter. We investigate the model via the mean-field approach, which gives the exact result in the case of a complete graph, as well as via Monte Carlo simulations. Additionally, we provide a heuristic reasoning, which explains observed phenomena. We show that indeed if the threshold , which corresponds to the majority-vote model, an order-disorder transition is continuous. Moreover, results obtained for both versions of the model (one with independence and the second one with anticonformity) give the same results, only rescaled by the factor of 2. However, for the jump of the order parameter and the hysteresis is observed for the model with independence, and both versions of the model give qualitatively different results.

Suggested Citation

  • Bartłomiej Nowak & Katarzyna Sznajd-Weron, 2019. "Homogeneous Symmetrical Threshold Model with Nonconformity: Independence versus Anticonformity," Complexity, Hindawi, vol. 2019, pages 1-14, April.
  • Handle: RePEc:hin:complx:5150825
    DOI: 10.1155/2019/5150825
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    References listed on IDEAS

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    1. Andrew E. Clark, 2003. "Unemployment as a Social Norm: Psychological Evidence from Panel Data," Journal of Labor Economics, University of Chicago Press, vol. 21(2), pages 289-322, April.
    2. Nyczka, Piotr & Cisło, Jerzy & Sznajd-Weron, Katarzyna, 2012. "Opinion dynamics as a movement in a bistable potential," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 317-327.
    3. Vieira, Allan R. & Crokidakis, Nuno, 2016. "Phase transitions in the majority-vote model with two types of noises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 30-36.
    4. Byrka, Katarzyna & Jȩdrzejewski, Arkadiusz & Sznajd-Weron, Katarzyna & Weron, Rafał, 2016. "Difficulty is critical: The importance of social factors in modeling diffusion of green products and practices," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 723-735.
    5. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    6. Serge Galam, 2008. "Sociophysics: A Review Of Galam Models," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 19(03), pages 409-440.
    7. Katarzyna Sznajd-Weron & Janusz Szwabinski & Rafal Weron & Tomasz Weron, 2013. "Rewiring the network. What helps an innovation to diffuse?," HSC Research Reports HSC/13/09, Hugo Steinhaus Center, Wroclaw University of Technology.
    8. Katarzyna Sznajd-Weron & Józef Sznajd, 2000. "Opinion Evolution In Closed Community," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 11(06), pages 1157-1165.
    9. Encinas, J.M. & Chen, Hanshuang & de Oliveira, Marcelo M. & Fiore, Carlos E., 2019. "Majority vote model with ancillary noise in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 563-570.
    10. Jędrzejewski, Arkadiusz & Sznajd-Weron, Katarzyna, 2018. "Impact of memory on opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 306-315.
    11. Weron, Tomasz & Kowalska-Pyzalska, Anna & Weron, Rafał, 2018. "The role of educational trainings in the diffusion of smart metering platforms: An agent-based modeling approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 591-600.
    12. Duncan J. Watts & Peter Sheridan Dodds, 2007. "Influentials, Networks, and Public Opinion Formation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 34(4), pages 441-458, May.
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    Cited by:

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    3. Oestereich, A.L. & Pires, M.A. & Duarte Queirós, S.M. & Crokidakis, N., 2020. "Hysteresis and disorder-induced order in continuous kinetic-like opinion dynamics in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    4. Takamitsu Watanabe, 2020. "A numerical study on efficient jury size," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-7, December.
    5. Galam, Serge, 2021. "Will Trump win again in the 2020 election? An answer from a sociophysics model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    6. Lee, Kyu-Min & Lee, Sungmin & Min, Byungjoon & Goh, K.-I., 2023. "Threshold cascade dynamics on signed random networks," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    7. Michel Grabisch & Fen Li, 2020. "Anti-conformism in the Threshold Model of Collective Behavior," Dynamic Games and Applications, Springer, vol. 10(2), pages 444-477, June.
    8. A. Krawiecki, 2021. "Ferromagnetic and spin-glass like transition in the q-neighbor Ising model on random graphs," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(3), pages 1-15, March.

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