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Influence in social networks with stubborn agents: From competition to bargaining

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  • Kareeva, Yulia
  • Sedakov, Artem
  • Zhen, Mengke

Abstract

The literature on game-theoretic models of opinion dynamics in social networks mainly focuses on the Nash equilibrium, which reflects a competitive situation between influencing agents called players. In some real-world situations, however, players negotiate over a game; thus, a different type of solution needs to be considered to account for possible outcomes. In this paper, we examine an opinion dynamics game based on the Friedkin–Johnsen model for which we characterize the Pareto frontier, including the Nash bargaining solution. Next, we analyze this solution when there are changes in the susceptibility of noninfluencing agents with respect to their initial opinions. We also quantify how the Nash equilibrium outcome differs from the outcome prescribed by the Nash bargaining solution.

Suggested Citation

  • Kareeva, Yulia & Sedakov, Artem & Zhen, Mengke, 2023. "Influence in social networks with stubborn agents: From competition to bargaining," Applied Mathematics and Computation, Elsevier, vol. 444(C).
  • Handle: RePEc:eee:apmaco:v:444:y:2023:i:c:s009630032200858x
    DOI: 10.1016/j.amc.2022.127790
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    References listed on IDEAS

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    1. 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.
    2. Daron Acemoglu & Asuman Ozdaglar, 2011. "Opinion Dynamics and Learning in Social Networks," Dynamic Games and Applications, Springer, vol. 1(1), pages 3-49, March.
    3. Tamer Başar & Quanyan Zhu, 2011. "Prices of Anarchy, Information, and Cooperation in Differential Games," Dynamic Games and Applications, Springer, vol. 1(1), pages 50-73, March.
    4. Alain Haurie & Jacek B Krawczyk & Georges Zaccour, 2012. "Games and Dynamic Games," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8442, February.
    5. Vladimir Mazalov & Elena Parilina, 2020. "The Euler-Equation Approach in Average-Oriented Opinion Dynamics," Mathematics, MDPI, vol. 8(3), pages 1-16, March.
    6. Bindel, David & Kleinberg, Jon & Oren, Sigal, 2015. "How bad is forming your own opinion?," Games and Economic Behavior, Elsevier, vol. 92(C), pages 248-265.
    7. Wang, Shaoli & Rong, Libin & Wu, Jianhong, 2016. "Bistability and multistability in opinion dynamics models," Applied Mathematics and Computation, Elsevier, vol. 289(C), pages 388-395.
    8. Fang, Aili, 2021. "The influence of communication structure on opinion dynamics in social networks with multiple true states," Applied Mathematics and Computation, Elsevier, vol. 406(C).
    9. Zheng, Xi & Lu, Xi & Chan, Felix T.S. & Deng, Yong & Wang, Zhen, 2015. "Bargaining models in opinion dynamics," Applied Mathematics and Computation, Elsevier, vol. 251(C), pages 162-168.
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