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Exploring the trust management mechanism in self-organizing complex network based on game theory

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  • Li, Hui-Jia
  • Wang, Qian
  • Liu, Shenfeng
  • Hu, Jun

Abstract

The trust management mechanism can used to establish cooperation relationship between nodes and decrease risk caused by the information interaction. In this paper, we propose a new trust management mechanism based on game theory to model the acquisition of indirect trust information between nodes, and put forward the corresponding punishment mechanism to enhance the cooperative behaviors. Moreover, single stage and repeated game model are established respectively to study the evolutionary stable strategy with different parameters, which illustrate the evolutionary selection process of node’s strategies in the indirect information assessment procedure. By the continuous adjustment of strategies, the whole network will converge to a stable state finally. The experiment results validate that the effectiveness and efficiency of the proposed model, which can promote the cooperate behavior with the neighbor nodes to obtain the maximum benefit when facing the indirect information assessment. Furthermore, the stability of the trust management system can also be evaluated and enhanced, which help us to design the effective trust management mechanism in the real world.

Suggested Citation

  • Li, Hui-Jia & Wang, Qian & Liu, Shenfeng & Hu, Jun, 2020. "Exploring the trust management mechanism in self-organizing complex network based on game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    • Handle: RePEc:eee:phsmap:v:542:y:2020:i:c:s0378437119319600
    DOI: 10.1016/j.physa.2019.123514
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    References listed on IDEAS

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    1. Peican Zhu & Yangming Guo & Shubin Si & Jie Han, 2017. "A stochastic analysis of competing failures with propagation effects in functional dependency gates," IISE Transactions, Taylor & Francis Journals, vol. 49(11), pages 1050-1064, November.
    2. repec:nas:journl:v:115:y:2018:p:30-35 is not listed on IDEAS
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    Cited by:

    1. Deng, Ziwei & Li, Yuxuan & Zhu, Hongqiu & Huang, Keke & Tang, Zhaohui & Wang, Zhen, 2020. "Sparse stacked autoencoder network for complex system monitoring with industrial applications," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    2. Yang, Wenjuan & Zhang, Jiantong & Yan, Hong, 2022. "Promotions of online reviews from a channel perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    3. Chen, Hailiang & Chen, Bin & Ai, Chuan & Zhu, Mengna & Qiu, Xiaogang, 2022. "The evolving network model with community size and distance preferences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    4. Cullen, Andrew C. & Alpcan, Tansu & Kalloniatis, Alexander C., 2022. "Adversarial decisions on complex dynamical systems using game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    5. Zulfiqar, M. & Kamran, M. & Rasheed, M.B., 2022. "A blockchain-enabled trust aware energy trading framework using games theory and multi-agent system in smat grid," Energy, Elsevier, vol. 255(C).
    6. Quan, Ji & Pu, Zhenjuan & Wang, Xianjia, 2021. "Comparison of social exclusion and punishment in promoting cooperation: Who should play the leading role?," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).

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