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Evolutionary Game Dynamics of the Competitive Information Propagation on Social Networks

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  • Xue Yang
  • Zhiliang Zhu
  • Hai Yu
  • Yuli Zhao
  • Li Guo

Abstract

To better control the scope of information propagation and understand its dynamic characteristics, we propose an information propagation model based on evolutionary game theory. The model can simulate an individual’s strategy selection in social networks when facing two pieces of competitive information, whereby “competitive information” is defined as two pieces of information which have the opposite meaning. First, a reasonable payoff function is designed for individuals based on pairwise interaction. Second, each individual selects a friend it trusts. Third, a probability value is used to indicate whether an individual imitates the strategy of the selected friend. In the model, we consider not only the heterogeneous influence of friends’ strategies on individual decision-making in the process of communication but also the attenuation of individuals’ attention to information when information about friends is received repeatedly. The simulation results show that our model can accurately simulate the propagation of two pieces of competitive information. Furthermore, we find that the basic payoff that accrues to individuals as a result of spreading their information and the network topology are two factors that significantly influence the propagation result. The results provide effective insights into how to better control and guide public opinion.

Suggested Citation

  • Xue Yang & Zhiliang Zhu & Hai Yu & Yuli Zhao & Li Guo, 2019. "Evolutionary Game Dynamics of the Competitive Information Propagation on Social Networks," Complexity, Hindawi, vol. 2019, pages 1-11, December.
  • Handle: RePEc:hin:complx:8385426
    DOI: 10.1155/2019/8385426
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    References listed on IDEAS

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    Cited by:

    1. Alam, Muntasir & Ida, Yuki & Tanimoto, Jun, 2021. "Abrupt epidemic outbreak could be well tackled by multiple pre-emptive provisions-A game approach considering structured and unstructured populations," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).

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