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Coevolution of Cooperation and Complex Networks via Indirect Reciprocity

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10638))

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Abstract

Most previous research on indirect reciprocity was in well-mixed population. Distinguishing the interacting network from learning network provides a chance to study indirect reciprocity in networks. Unlike previous research, we propose a coevolution model of cooperation and complex networks via indirect reciprocity, where an individual can interact globally but update strategy locally. Based on this model, we describe the simulation results of coevolution, including the effects of rewiring mechanism on the evolution of cooperation, and how the evolution of cooperation affects networks restructure. Results show that rewiring mechanism favors the evolution of cooperation and the evolution of cooperation can restructure social networks. To understand and explain the results in detail, we graphically depict the snapshots of coevolution process. These findings facilitate us to further understand the evolution of cooperation and the restructure of complex networks.

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Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities (No. FRF-TP-15-116A1), the China Postdoctoral Science Foundation (No. 2015M580989), and the National Natural Science Foundation of China (Nos. 61603036, 61520106009 and 61533008).

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Authors and Affiliations

  1. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Aizhi Liu, Lei Wang, Yanling Zhang & Changyin Sun

  2. School of Automation, Southeast University, Nanjing, 210096, China

    Aizhi Liu, Lei Wang, Yanling Zhang & Changyin Sun

Authors
  1. Aizhi Liu
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  2. Lei Wang
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  3. Yanling Zhang
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  4. Changyin Sun
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Corresponding author

Correspondence to Changyin Sun .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Liu, A., Wang, L., Zhang, Y., Sun, C. (2017). Coevolution of Cooperation and Complex Networks via Indirect Reciprocity. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_93

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  • DOI: https://doi.org/10.1007/978-3-319-70139-4_93

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70138-7

  • Online ISBN: 978-3-319-70139-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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