Opinion dynamics under the influence of radical groups, charismatic leaders, and other constant signals: A simple unifying model

  • Received: 01 December 2014 Revised: 01 March 2015
  • Primary: 90B10, 90B18, 91D30, 37N40; Secondary: 15B51, 39A20.

  • By a simple extension of the bounded confidence model, it is possible to model the influence of a radical group, or a charismatic leader on the opinion dynamics of `normal' agents that update their opinions under both, the influence of their normal peers, and the additional influence of the radical group or a charismatic leader. From a more abstract point of view, we model the influence of a signal, that is constant, may have different intensities, and is `heard' only by agents with opinions, that are not too far away. For such a dynamic a Constant Signal Theorem is proven. In the model we get a lot of surprising effects. For instance, the more intensive signal may have less effect; more radicals may lead to less radicalization of normal agents. The model is an extremely simple conceptual model. Under some assumptions the whole parameter space can be analyzed. The model inspires new possible explanations, new perspectives for empirical studies, and new ideas for prevention or intervention policies.

    Citation: Rainer Hegselmann, Ulrich Krause. Opinion dynamics under the influence of radical groups, charismatic leaders, and other constant signals: A simple unifying model[J]. Networks and Heterogeneous Media, 2015, 10(3): 477-509. doi: 10.3934/nhm.2015.10.477

    Related Papers:

    [1] Rainer Hegselmann, Ulrich Krause . Opinion dynamics under the influence of radical groups, charismatic leaders, and other constant signals: A simple unifying model. Networks and Heterogeneous Media, 2015, 10(3): 477-509. doi: 10.3934/nhm.2015.10.477
    [2] Sergei Yu. Pilyugin, Maria S. Tarasova, Aleksandr S. Tarasov, Grigorii V. Monakov . A model of voting dynamics under bounded confidence with nonstandard norming. Networks and Heterogeneous Media, 2022, 17(6): 917-931. doi: 10.3934/nhm.2022032
    [3] Sergei Yu. Pilyugin, M. C. Campi . Opinion formation in voting processes under bounded confidence. Networks and Heterogeneous Media, 2019, 14(3): 617-632. doi: 10.3934/nhm.2019024
    [4] Michael Herty, Lorenzo Pareschi, Giuseppe Visconti . Mean field models for large data–clustering problems. Networks and Heterogeneous Media, 2020, 15(3): 463-487. doi: 10.3934/nhm.2020027
    [5] Clinton Innes, Razvan C. Fetecau, Ralf W. Wittenberg . Modelling heterogeneity and an open-mindedness social norm in opinion dynamics. Networks and Heterogeneous Media, 2017, 12(1): 59-92. doi: 10.3934/nhm.2017003
    [6] Sharayu Moharir, Ananya S. Omanwar, Neeraja Sahasrabudhe . Diffusion of binary opinions in a growing population with heterogeneous behaviour and external influence. Networks and Heterogeneous Media, 2023, 18(3): 1288-1312. doi: 10.3934/nhm.2023056
    [7] Yuntian Zhang, Xiaoliang Chen, Zexia Huang, Xianyong Li, Yajun Du . Managing consensus based on community classification in opinion dynamics. Networks and Heterogeneous Media, 2023, 18(2): 813-841. doi: 10.3934/nhm.2023035
    [8] Marina Dolfin, Mirosław Lachowicz . Modeling opinion dynamics: How the network enhances consensus. Networks and Heterogeneous Media, 2015, 10(4): 877-896. doi: 10.3934/nhm.2015.10.877
    [9] Sabrina Bonandin, Mattia Zanella . Effects of heterogeneous opinion interactions in many-agent systems for epidemic dynamics. Networks and Heterogeneous Media, 2024, 19(1): 235-261. doi: 10.3934/nhm.2024011
    [10] Aylin Aydoğdu, Sean T. McQuade, Nastassia Pouradier Duteil . Opinion Dynamics on a General Compact Riemannian Manifold. Networks and Heterogeneous Media, 2017, 12(3): 489-523. doi: 10.3934/nhm.2017021
  • By a simple extension of the bounded confidence model, it is possible to model the influence of a radical group, or a charismatic leader on the opinion dynamics of `normal' agents that update their opinions under both, the influence of their normal peers, and the additional influence of the radical group or a charismatic leader. From a more abstract point of view, we model the influence of a signal, that is constant, may have different intensities, and is `heard' only by agents with opinions, that are not too far away. For such a dynamic a Constant Signal Theorem is proven. In the model we get a lot of surprising effects. For instance, the more intensive signal may have less effect; more radicals may lead to less radicalization of normal agents. The model is an extremely simple conceptual model. Under some assumptions the whole parameter space can be analyzed. The model inspires new possible explanations, new perspectives for empirical studies, and new ideas for prevention or intervention policies.


    [1] D. Acemoglu and A. Ozdaglar, Opinion dynamics and learning in social networks, Dynamic Games and Applications, 1 (2011), 3-49. doi: 10.1007/s13235-010-0004-1
    [2] M. Baurmann, G. Betz and R. Cramm, Meinungsdynamiken in fundamentalistischen Gruppen - Erklärungshypothesen auf der Basis von Simulationsmodellen, Analyse and Kritik, 36 (2014), 61-102.
    [3] V. D. Blondel, J. M. Hendrickx and J. N. Tsitsiklis, Continuous-time average-preserving opinion dynamics with opinion-dependent communications, SIAM Journal on Control and Optimization, 48 (2010), 5214-5240, doi: 10.1137/090766188
    [4] B. Chazelle, The total s-energy of a multiagent system, SIAM Journal of Control and Optimization, 49 (2011), 1680-1706. doi: 10.1137/100791671
    [5] G. Deffuant, F. Amblard, G. Weisbuch and T. Faure, How can extremism prevail? A study based on the relative agreement interaction model, Journal of Artificial Societies and Social Simulation, 5 (2002). Available from: http://jasss.soc.surrey.ac.uk/5/4/1.html.
    [6] G. Deffuant, D. Neau, F. Amblard and G. Weisbuch, Mixing beliefs among interacting agents, Advances in Complex Systems, 3 (2000), 87-98. doi: 10.1142/S0219525900000078
    [7] M. H. DeGroot, Reaching a consensus, Journal of the American Statistical Association, 69 (1974), 118-121. doi: 10.1080/01621459.1974.10480137
    [8] R. Hegselmann, Bounded confidence, radical groups, and charismatic leaders, in Advances in Computational Social Science and Social Simulation. Proceedings of the Social Simulation Conference 2014 Barcelona, Catalunya (Spain), September 15 (eds. F. J. Miguel, F. Amblard, J. A. Barceló and M. Madella), Autònoma University of Barcelona, (DDD repository http://ddd.uab.cat/record/125597), Barcelona, 2014, 217-219.
    [9] R. Hegselmann, S. König, S. Kurz, C. Niemann and J. Rambau, Optimal opinion control: The campaign problem, Journal of Artificial Societies and Social Simulation (JASSS), (2015), 47pp. doi: 10.2139/ssrn.2516866
    [10] R. Hegselmann and U. Krause, Opinion dynamics and bounded confidence: Models, analysis and simulation, Journal of Artificial Societies and Social Simulation, 5 (2002). Available from: http://jasss.soc.surrey.ac.uk/5/3/2.html.
    [11] R. Hegselmann and U. Krause, Truth and cognitive division of labour: First steps towards a computer aided social epistemology, Journal of Artificial Societies and Social Simulation, 9 (2006). Available from: http://jasss.soc.surrey.ac.uk/9/3/10.html.
    [12] S. Huet, G. Deffuant and W. Jager, Rejection mechanism in 2d bounded confidence provides more conformity, Advances in Complex Systems, 11 (2008), 529-549. doi: 10.1142/S0219525908001799
    [13] U. Krause, Positive Dynamical Systems in Discrete Time. Theory, Models, and Applications, De Gruyter, Berlin, 2015. doi: 10.1515/9783110365696
    [14] S. Kurz and J. Rambau, On the Hegselmann-Krause conjecture in opinion dynamics, Journal of Difference Equations and Applications, 17 (2011), 859-876. doi: 10.1080/10236190903443129
    [15] J. Lorenz, Continuous opinion dynamics under bounded confidence: A survey, International Journal of Modern Physics C, 18 (2007), 1819-1838. doi: 10.1142/S0129183107011789
    [16] N. Oreskes and E. M. Conway, Merchants of Doubt - How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Global Warming, Bloomsbury Press, 2010.
    [17] G. G. Polhill, L. R. Izquierdo and N. M. Gotts, The ghost in the model (and other effects of floating point arithmetic), Journal of Artificial Societies and Social Simulation, 8 (2005). Available from: http://jasss.soc.surrey.ac.uk/8/1/5.html.
    [18] S. Wongkaew, M. Caponigro and A. Borzí, On the control through leadership of the Hegselmann-Krause opinion formation model, Mathematical Models and Methods in Applied Sciences, 25 (2015), 565-585. doi: 10.1142/S0218202515400060
    [19] H. Xia, H. Wang and Z. Xuan, Opinion dynamics: A multidisciplinary review and perspective on future research, International Journal of Knowledge and Systems Science, 2 (2011), 72-91. doi: 10.4018/978-1-4666-3998-0.ch021
  • This article has been cited by:

    1. Mohamad Amin Sharifi Kolarijani, Anton V. Proskurnikov, Peyman Mohajerin Esfahani, Macroscopic Noisy Bounded Confidence Models With Distributed Radical Opinions, 2021, 66, 0018-9286, 1174, 10.1109/TAC.2020.2994284
    2. Catherine A. Glass, David H. Glass, Opinion dynamics of social learning with a conflicting source, 2021, 563, 03784371, 125480, 10.1016/j.physa.2020.125480
    3. B. Aylaj, N. Bellomo, N. Chouhad, D. Knopoff, On the Interaction Between Soft and Hard Sciences: the Role of Mathematical Sciences, 2021, 49, 2305-221X, 3, 10.1007/s10013-019-00381-3
    4. Li-Xin Wang, Modeling Stock Price Dynamics With Fuzzy Opinion Networks, 2017, 25, 1063-6706, 277, 10.1109/TFUZZ.2016.2574911
    5. Dong Xue, Sandra Hirche, Ming Cao, Opinion Behavior Analysis in Social Networks Under the Influence of Coopetitive Media, 2020, 7, 2327-4697, 961, 10.1109/TNSE.2019.2894565
    6. Christoph Merdes, Strategy and the pursuit of truth, 2021, 198, 0039-7857, 117, 10.1007/s11229-018-01985-x
    7. Jose Segovia-Martin, Monica Tamariz, Marton Karsai, Synchronising institutions and value systems: A model of opinion dynamics mediated by proportional representation, 2021, 16, 1932-6203, e0257525, 10.1371/journal.pone.0257525
    8. Yajing Wang, Jingwen Yi, Li Chai, 2022, Evolution analysis of two-layer time-varying trust Hegselmann-Krause models with opinion leaders, 978-988-75815-3-6, 6888, 10.23919/CCC55666.2022.9902456
    9. Michael Baurmann, Gregor Betz, Rainer Cramm, 2018, Chapter 10, 978-3-319-61069-6, 259, 10.1007/978-3-319-61070-2_10
    10. Catherine A. Glass, David H. Glass, Social Influence of Competing Groups and Leaders in Opinion Dynamics, 2021, 58, 0927-7099, 799, 10.1007/s10614-020-10049-7
    11. A G Chkhartishvili, D A Gubanov, On the Concept of an Informational Community in a Social Network, 2021, 1864, 1742-6588, 012052, 10.1088/1742-6596/1864/1/012052
    12. Rainer Hegselmann, Ulrich Krause, Consensus and Fragmentation of Opinions with a Focus on Bounded Confidence, 2019, 126, 0002-9890, 700, 10.1080/00029890.2019.1626685
    13. Li-Xin Wang, Jerry M Mendel, Fuzzy Opinion Networks: A Mathematical Framework for the Propagation of Opinions and Their Uncertainties Across Social Networks, 2014, 1556-5068, 10.2139/ssrn.2538553
    14. N. Bellomo, F. Brezzi, M. Pulvirenti, Modeling behavioral social systems, 2017, 27, 0218-2025, 1, 10.1142/S0218202517020018
    15. M. A. S. Kolarijani, A. V. Proskurnikov, P. Mohajerin Esfahani, 2019, Long-term Behavior of Mean-field Noisy Bounded Confidence Models with Distributed Radicals, 978-1-7281-1398-2, 6158, 10.1109/CDC40024.2019.9030212
    16. Chaoqian Wang, Opinion Dynamics with Higher-Order Bounded Confidence, 2022, 24, 1099-4300, 1300, 10.3390/e24091300
    17. Ricardo Almeida, Rafał Kamocki, Agnieszka B. Malinowska, Tatiana Odzijewicz, Optimal leader-following consensus of fractional opinion formation models, 2021, 381, 03770427, 112996, 10.1016/j.cam.2020.112996
    18. D. A. Gubanov, I. V. Petrov, A. G. Chkhartishvili, Multidimensional Model of Opinion Dynamics in Social Networks: Polarization Indices, 2021, 82, 0005-1179, 1802, 10.1134/S0005117921100167
    19. André C.R. Martins, Extremism definitions in opinion dynamics models, 2022, 589, 03784371, 126623, 10.1016/j.physa.2021.126623
    20. Ricardo Almeida, Agnieszka B. Malinowska, Tatiana Odzijewicz, 2018, Fractional Opinion Formation Models with Leadership, 978-1-5386-4325-9, 259, 10.1109/MMAR.2018.8486016
    21. Nataša Djurdjevac Conrad, Jonas Köppl, Ana Djurdjevac, Feedback Loops in Opinion Dynamics of Agent-Based Models with Multiplicative Noise, 2022, 24, 1099-4300, 1352, 10.3390/e24101352
    22. Yajing Wang, Jingwen Yi, Li Chai, 2022, Evolution analysis of the time-varying trust Hegselmann-Krause models, 978-1-6654-7896-0, 5415, 10.1109/CCDC55256.2022.10034009
    23. Marijn A. Keijzer, Michael Mäs, The strength of weak bots, 2021, 21, 24686964, 100106, 10.1016/j.osnem.2020.100106
    24. Honglin Bao, Zachary P Neal, Wolfgang Banzhaf, Coevolutionary opinion dynamics with sparse interactions in open-ended societies, 2023, 9, 2199-4536, 565, 10.1007/s40747-022-00810-w
    25. Koresh Khateri, Mahdi Pourgholi, Mohsen Montazeri, Lorenzo Sabattini, 2019, Effect of Stubborn Agents on Bounded Confidence Opinion Dynamic systems: Unanimity in Presence of Stubborn Agents, 978-1-7281-1508-5, 875, 10.1109/IranianCEE.2019.8786437
    26. G. Ajmone Marsan, N. Bellomo, L. Gibelli, Stochastic evolutionary differential games toward a systems theory of behavioral social dynamics, 2016, 26, 0218-2025, 1051, 10.1142/S0218202516500251
    27. Hossein Noorazar, Recent advances in opinion propagation dynamics: a 2020 survey, 2020, 135, 2190-5444, 10.1140/epjp/s13360-020-00541-2
    28. G. Jordan Maclay, Moody Ahmad, Gareth J. Baxter, An agent based force vector model of social influence that predicts strong polarization in a connected world, 2021, 16, 1932-6203, e0259625, 10.1371/journal.pone.0259625
    29. Scott A. Condie, Corrine M. Condie, Stochastic events can explain sustained clustering and polarisation of opinions in social networks, 2021, 11, 2045-2322, 10.1038/s41598-020-80353-7
    30. Fatma Ataş, Ali Demirci, Cihangir Özemir, Bifurcation analysis of Friedkin–Johnsen and Hegselmann–Krause models with a nonlinear interaction potential, 2021, 185, 03784754, 676, 10.1016/j.matcom.2021.01.012
    31. Pawel Sobkowicz, Whither Now, Opinion Modelers?, 2020, 8, 2296-424X, 10.3389/fphy.2020.587009
    32. Koresh Khateri, Mahdi Pourgholi, Mohsen Montazeri, Lorenzo Sabattini, 2019, Effect of Stubborn Agents on Bounded Confidence Opinion Dynamic Systems: Unanimity in Presence of Stubborn Agents, 978-1-7281-1508-5, 875, 10.1109/IranianCEE.2019.8786531
    33. Ulrike Hahn, Collectives and Epistemic Rationality, 2022, 14, 1756-8757, 602, 10.1111/tops.12610
    34. Jean-Denis Mathias, Sylvie Huet, Guillaume Deffuant, An energy-like indicator to assess opinion resilience, 2017, 473, 03784371, 501, 10.1016/j.physa.2016.12.035
    35. Han L J van der Maas, Jonas Dalege, Lourens Waldorp, Yamir Moreno, The polarization within and across individuals: the hierarchical Ising opinion model, 2020, 8, 2051-1310, 10.1093/comnet/cnaa010
    36. D A Gubanov, A study of a complex model of opinion dynamics in social networks, 2021, 1740, 1742-6588, 012040, 10.1088/1742-6596/1740/1/012040
    37. Heather Z. Brooks, Mason A. Porter, A model for the influence of media on the ideology of content in online social networks, 2020, 2, 2643-1564, 10.1103/PhysRevResearch.2.023041
    38. Ricardo Almeida, Agnieszka B. Malinowska, Tatiana Odzijewicz, Optimal Leader–Follower Control for the Fractional Opinion Formation Model, 2019, 182, 0022-3239, 1171, 10.1007/s10957-018-1363-9
    39. B D Goddard, B Gooding, H Short, G A Pavliotis, Noisy bounded confidence models for opinion dynamics: the effect of boundary conditions on phase transitions, 2022, 87, 0272-4960, 80, 10.1093/imamat/hxab044
    40. Yi Lu, Yiyi Zhao, Jiangbo Zhang, Jiangping Hu, Xiaoming Hu, 2019, Fuzzy Hegselmann-Krause Opinion Dynamics with Opinion Leaders, 978-9-8815-6397-2, 6019, 10.23919/ChiCC.2019.8865519
    41. N. Bellomo, M. Esfahanian, V. Secchini, P. Terna, What is life? Active particles tools towards behavioral dynamics in social-biology and economics, 2022, 43, 15710645, 189, 10.1016/j.plrev.2022.10.001
    42. GIULIA BRAGHINI, FRANCESCO SALVARANI, EFFECTS OF HIDDEN OPINION MANIPULATION IN MICROBLOGGING PLATFORMS, 2021, 24, 0219-5259, 10.1142/S0219525921500090
    43. Li-Xin Wang, Modeling Stock Price Dynamics with Fuzzy Opinion Networks, 2015, 1556-5068, 10.2139/ssrn.2645196
    44. Ulrich Krause, Reply on Comments on “Opinion Dynamics Driven by Various Ways of Averaging” by Youzong Xu and Yunfei Cao, 2020, 55, 0927-7099, 327, 10.1007/s10614-018-9873-y
    45. Jing Cao, Xuan-hua Xu, Fei Dai, Bin Pan, An evolution model of risk preference influenced by extremists in large group emergency consensus process, 2020, 39, 10641246, 7733, 10.3233/JIFS-201106
    46. Li-Xin Wang, Jerry M. Mendel, Fuzzy Opinion Networks: A Mathematical Framework for the Evolution of Opinions and Their Uncertainties Across Social Networks, 2016, 24, 1063-6706, 880, 10.1109/TFUZZ.2015.2486816
    47. Yuri F. Saporito, M. O. Souza, Y. Thamsten, A Mathematical Framework for Dynamical Social Interactions with Dissimulation, 2023, 33, 0938-8974, 10.1007/s00332-022-09867-w
    48. Josselin Garnier, George Papanicolaou, Tzu-Wei Yang, Consensus Convergence with Stochastic Effects, 2017, 45, 2305-221X, 51, 10.1007/s10013-016-0190-2
    49. SYLVIE HUET, JEAN-DENIS MATHIAS, FEW SELF-INVOLVED AGENTS AMONG BOUNDED CONFIDENCE AGENTS CAN CHANGE NORMS, 2018, 21, 0219-5259, 1850007, 10.1142/S0219525918500078
    50. Simon Schweighofer, David Garcia, Frank Schweitzer, An agent-based model of multi-dimensional opinion dynamics and opinion alignment, 2020, 30, 1054-1500, 093139, 10.1063/5.0007523
    51. Dmitry Rabinovich, Alfred M. Bruckstein, Erratic Extremism Causes Dynamic Consensus: A New Model for Opinion Dynamics, 2021, 20, 1536-0040, 2077, 10.1137/20M1385937
    52. Li-Xin Wang, Hierarchical Fuzzy Opinion Networks: Top-Down for Social Organizations and Bottom-Up for Election, 2020, 1063-6706, 1, 10.1109/TFUZZ.2020.2965864
    53. Shuwei Chen, David H. Glass, Mark McCartney, How Opinion Leaders Affect Others on Seeking Truth in a Bounded Confidence Model, 2020, 12, 2073-8994, 1362, 10.3390/sym12081362
    54. Kit Ming Danny Chan, Robert Duivenvoorden, Andreas Flache, Michel Mandjes, A relative approach to opinion formation, 2022, 0022-250X, 1, 10.1080/0022250X.2022.2036142
    55. Anton V. Proskurnikov, Roberto Tempo, A tutorial on modeling and analysis of dynamic social networks. Part I, 2017, 43, 13675788, 65, 10.1016/j.arcontrol.2017.03.002
    56. Anton V. Proskurnikov, Roberto Tempo, A tutorial on modeling and analysis of dynamic social networks. Part II, 2018, 45, 13675788, 166, 10.1016/j.arcontrol.2018.03.005
    57. Dana Warmsley, Samuel D. Johnson, 2021, Influence in Transient Populations, 978-1-6654-3902-2, 2685, 10.1109/BigData52589.2021.9671874
    58. Brandon Boesch, A concrete example of representational licensing: The Mississippi River Basin Model, 2022, 92, 00393681, 36, 10.1016/j.shpsa.2022.01.002
    59. N. Pescetelli, D. Barkoczi, M. Cebrian, Bots influence opinion dynamics without direct human-bot interaction: the mediating role of recommender systems, 2022, 7, 2364-8228, 10.1007/s41109-022-00488-6
    60. Bernard Chazelle, Quansen Jiu, Qianxiao Li, Chu Wang, Well-posedness of the limiting equation of a noisy consensus model in opinion dynamics, 2017, 263, 00220396, 365, 10.1016/j.jde.2017.02.036
    61. Igor Douven, Rainer Hegselmann, Mis- and disinformation in a bounded confidence model, 2021, 291, 00043702, 103415, 10.1016/j.artint.2020.103415
    62. M. Dolfin, L. Leonida, N. Outada, Modeling human behavior in economics and social science, 2017, 22-23, 15710645, 1, 10.1016/j.plrev.2017.06.026
    63. Kamal S Selim, Ahmed E Okasha, Fatma R Farag, Measuring the role of two competing groups of informed agents in opinion formation, 2019, 95, 0037-5497, 753, 10.1177/0037549718800583
    64. Clinton Innes, Razvan C. Fetecau, Ralf W. Wittenberg, Modelling heterogeneity and an open-mindedness social norm in opinion dynamics, 2017, 12, 1556-181X, 59, 10.3934/nhm.2017003
    65. Igor Douven, Gerhard Schurz, Integrating individual and social learning: accuracy and evolutionary viability, 2022, 1381-298X, 10.1007/s10588-022-09372-1
    66. Trisha Srivastava, Carmela Bernardo, Claudio Altafini, Francesco Vasca, 2023, Analyzing the effects of confidence thresholds on opinion clustering in homogeneous Hegselmann–Krause models, 979-8-3503-1543-1, 587, 10.1109/MED59994.2023.10185838
    67. William H. Warren, J. Benjamin Falandays, Kei Yoshida, Trenton D. Wirth, Brian A. Free, Human Crowds as Social Networks: Collective Dynamics of Consensus and Polarization, 2023, 1745-6916, 10.1177/17456916231186406
    68. Igor Douven, Pandemics and flexible lockdowns: In praise of agent-based modeling, 2023, 13, 1879-4912, 10.1007/s13194-023-00541-w
    69. František Kalvas, Ashwin Ramaswamy, Michael D. Slater, 2023, Chapter 20, 978-3-031-34919-5, 249, 10.1007/978-3-031-34920-1_20
    70. Carmela Bernardo, Claudio Altafini, Anton Proskurnikov, Francesco Vasca, Bounded confidence opinion dynamics: A survey, 2024, 159, 00051098, 111302, 10.1016/j.automatica.2023.111302
    71. Armineh Rahmanian Kooshkaki, Sadegh Bolouki, S. Rasoul Etesami, Abolfazl Mohebbi, Partisan Confidence Model for Group Polarization, 2023, 2327-4697, 1, 10.1109/TNSE.2023.3255819
    72. Andrew Nugent, Susana N. Gomes, Marie-Therese Wolfram, On evolving network models and their influence on opinion formation, 2023, 456, 01672789, 133914, 10.1016/j.physd.2023.133914
    73. Luzie Helfmann, Nataša Djurdjevac Conrad, Philipp Lorenz-Spreen, Christof Schütte, Modelling opinion dynamics under the impact of influencer and media strategies, 2023, 13, 2045-2322, 10.1038/s41598-023-46187-9
    74. Carlos Andres Devia, Giulia Giordano, Probabilistic analysis of agent-based opinion formation models, 2023, 13, 2045-2322, 10.1038/s41598-023-46789-3
    75. Trisha Srivastava, Carmela Bernardo, Silvio Baccari, Francesco Vasca, 2023, An experimental verification for Hegselmann–Krause opinion dynamics, 979-8-3503-3798-3, 209, 10.1109/ICSTCC59206.2023.10308489
    76. Yujia Wu, Peng Guo, Modeling Misinformation Spread in a Bounded Confidence Model: A Simulation Study, 2024, 26, 1099-4300, 99, 10.3390/e26020099
    77. Franco Galante, Michele Garetto, Emilio Leonardi, 2024, Competition of Influencers: A Model for Maximizing Online Social Impact, 9798400703348, 343, 10.1145/3614419.3644031
    78. Carlos Andrés Devia, Giulia Giordano, Pierluigi Vellucci, Graphical analysis of agent-based opinion formation models, 2024, 19, 1932-6203, e0303204, 10.1371/journal.pone.0303204
    79. Heather Z. Brooks, Philip S. Chodrow, Mason A. Porter, Emergence of Polarization in a Sigmoidal Bounded-Confidence Model of Opinion Dynamics, 2024, 23, 1536-0040, 1442, 10.1137/22M1527258
    80. A. Nugent, S. N. Gomes, M. T. Wolfram, Steering opinion dynamics through control of social networks, 2024, 34, 1054-1500, 10.1063/5.0211026
    81. Hsiu-Chi Lu, Hsuan-wei Lee, Agents of Discord: Modeling the Impact of Political Bots on Opinion Polarization in Social Networks, 2024, 0894-4393, 10.1177/08944393241270382
    82. Igor Douven, Social Learning in Neural Agent-Based Models, 2024, 0031-8248, 1, 10.1017/psa.2024.33
    83. Teng Li, Andreas Flache, Wander Jager, How culture can affect opinion dynamics: the case of vaccination, 2025, 8, 2432-2717, 10.1007/s42001-024-00347-7
    84. A. Bautista, Opinion dynamics in bounded confidence models with manipulative agents: Moving the Overton window, 2025, 660, 03784371, 130379, 10.1016/j.physa.2025.130379
    85. Mehwish Nasim, Syed Muslim Gilani, Amin Qasmi, Usman Naseem, Simulating Influence Dynamics with LLM Agents, 2025, 2, 3067-2627, 10.70777/si.v2i1.13971
  • Reader Comments
  • © 2015 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(6412) PDF downloads(534) Cited by(85)

Article outline

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog