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The Impact of Network Degree Correlation on Parrondo’s Paradox

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Complex Networks and Their Applications VIII (COMPLEX NETWORKS 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 881))

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Abstract

A multi-agent Parrondo’s model based on complex networks is studied to analyze the influence of network degree correlation on Parrondo’s paradox. The model includes a zero-sum Game A, representing cooperation and competition behaviors between agents, and a two-branch Game B, representing the capital of a node and all of its neighbors. Then the parameter space of Parrondo’s paradox pertaining to cooperation and competition behavioral patterns, and the gradual change of the parameter space from an assortative random network to a disassortative random network is analyzed. The simulation results suggest that the size of the region of the parameter space that elicits Parrondo’s paradox is negatively correlated with the degree correlation of the network. For two distinct sets of probability parameters, the microcosmic reasons underlying the occurrence and non-occurrence of the paradox under the disassortative and assortative random network are elaborated, respectively. Common interaction mechanisms of the behavioral patterns, the asymmetric structure of Game B, and network topology are also revealed.

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Acknowledgments

This project was supported by the National Natural Science Foundation of China (Grant No. 11705002); Ministry of Education, Humanities and Social Sciences research projects (15YJCZH210; 19YJAZH098; 18YJCZH102).

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Authors and Affiliations

  1. School of Mechanical Engineering, Anhui University of Technology, Ma’anshan, 243002, Anhui, China

    Ye Ye & Lu Wang

  2. School of Management Science and Engineering, Anhui University of Technology, Ma’anshan, 243002, Anhui, China

    Xiao-Rong Hang, Lin Liu & Neng-gang Xie

Authors
  1. Ye Ye
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  2. Xiao-Rong Hang
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  3. Lin Liu
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  4. Lu Wang
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  5. Neng-gang Xie
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Corresponding author

Correspondence to Neng-gang Xie .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Università degli Studi di Milano, Milan, Italy

    Sabrina Gaito

  3. University of Aveiro, Aveiro, Portugal

    José Fernendo Mendes

  4. Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

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Ye, Y., Hang, XR., Liu, L., Wang, L., Xie, Ng. (2020). The Impact of Network Degree Correlation on Parrondo’s Paradox. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_40

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