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Public cooperation in two-layer networks with asymmetric interaction and learning environments

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  • Huang, Changwei
  • Han, Wenchen
  • Li, Haihong
  • Cheng, Hongyan
  • Dai, Qionglin
  • Yang, Junzhong

Abstract

Strategy updating is generally based on payoff comparison and strategy learning within the interaction pairs on networks in evolutionary games. In many previous works, the interaction and learning environments are assumed to be the same networks. However, in the real world, they might be different. In this work, we consider the spatial public goods game on two-layer networks, where the interaction and learning environments are represented by two asymmetric layers, respectively. We focus on the effects of edge overlap ω between the interaction and learning networks on the evolution of cooperation. The simulation results show that, the effects of ω on the evolution of cooperation depend on the synergy factor r. For relatively small r, higher overlap between the interaction and learning environments will be more favorable for cooperation. However, the situation is reverse for relatively large r, where the lower overlap between the interaction and learning environments results in higher level of cooperation. We also find that the asymmetry between the interaction and learning environments inhibits the coexistence of the cooperators and defectors. Furthermore, we show that the results of the model are robust to the underlying networks with different node degrees.

Suggested Citation

  • Huang, Changwei & Han, Wenchen & Li, Haihong & Cheng, Hongyan & Dai, Qionglin & Yang, Junzhong, 2019. "Public cooperation in two-layer networks with asymmetric interaction and learning environments," Applied Mathematics and Computation, Elsevier, vol. 340(C), pages 305-313.
  • Handle: RePEc:eee:apmaco:v:340:y:2019:i:c:p:305-313
    DOI: 10.1016/j.amc.2018.08.021
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Wang, Chaoqian & Sun, Chengbin, 2023. "Public goods game across multilayer populations with different densities," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    2. Liu, Danna & Huang, Changwei & Dai, Qionglin & Li, Haihong, 2019. "Positive correlation between strategy persistence and teaching ability promotes cooperation in evolutionary Prisoner’s Dilemma games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 267-274.
    3. Pan, Jianchen & Zhang, Lan & Han, Wenchen & Huang, Changwei, 2023. "Heterogeneous investment promotes cooperation in spatial public goods game on hypergraphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    4. Zhang, Lan & Pan, Jianchen & Huang, Changwei, 2023. "Effect of mixed random and directional migration on cooperation in the spatial prisoner’s dilemma," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    5. Wang, Mengyao & Pan, Qiuhui & He, Mingfeng, 2020. "The interplay of behaviors and attitudes in public goods game considering environmental investment," Applied Mathematics and Computation, Elsevier, vol. 382(C).
    6. Zhang, Lan & Xie, Yuan & Huang, Changwei & Li, Haihong & Dai, Qionglin, 2020. "Heterogeneous investments induced by historical payoffs promote cooperation in spatial public goods games," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    7. Shi, Zhenyu & Wei, Wei & Zheng, Hongwei & Zheng, Zhiming, 2023. "Bidirectional supervision: An effective method to suppress corruption and defection under the third party punishment mechanism of donation games," Applied Mathematics and Computation, Elsevier, vol. 450(C).
    8. Zou, Kuan & Han, Wenchen & Zhang, Lan & Huang, Changwei, 2024. "The spatial public goods game on hypergraphs with heterogeneous investment," Applied Mathematics and Computation, Elsevier, vol. 466(C).

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