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Utility Evaluation Based on One-To-N Mapping in the Prisoner’s Dilemma Game for Interdependent Networks

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  • Juan Wang
  • Wenwen Lu
  • Lina Liu
  • Li Li
  • Chengyi Xia

Abstract

In the field of evolutionary game theory, network reciprocity has become an important means to promote the level of promotion within the population system. Recently, the interdependency provides a novel perspective to understand the widespread cooperation behavior in many real-world systems. In previous works, interdependency is often built from the direct or indirect connections between two networks through the one-to-one mapping mode. However, under many realistic scenarios, players may need much more information from many neighboring agents so as to make a more rational decision. Thus, beyond the one-to-one mapping mode, we investigate the cooperation behavior on two interdependent lattices, in which the utility evaluation of a focal player on one lattice may not only concern himself, but also integrate the payoff information of several corresponding players on the other lattice. Large quantities of simulations indicate that the cooperation can be substantially promoted when compared to the traditionally spatial lattices. The cluster formation and phase transition are also analyzed in order to explore the role of interdependent utility coupling in the collective cooperation. Current results are beneficial to deeply understand various mechanisms to foster the cooperation exhibited inside natural, social and engineering systems.

Suggested Citation

  • Juan Wang & Wenwen Lu & Lina Liu & Li Li & Chengyi Xia, 2016. "Utility Evaluation Based on One-To-N Mapping in the Prisoner’s Dilemma Game for Interdependent Networks," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-14, December.
  • Handle: RePEc:plo:pone00:0167083
    DOI: 10.1371/journal.pone.0167083
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    Cited by:

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    2. Zhang, Yingchao & Ning, Hongyun & Wang, Juan & Xia, Chengyi, 2017. "Coveting the successful neighbor promotes the cooperation for the spatial public goods game on two-layered lattices," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 29-37.
    3. Wei Pan & Yi-Shin Chen, 2018. "Network approach for decision making under risk—How do we choose among probabilistic options with the same expected value?," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-19, April.
    4. Anastasia G Peshkovskaya & Tatiana S Babkina & Mikhail G Myagkov & Ivan A Kulikov & Ksenia V Ekshova & Kyle Harriff, 2017. "The socialization effect on decision making in the Prisoner's Dilemma game: An eye-tracking study," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-15, April.
    5. Takesue, Hirofumi, 2021. "Symmetry breaking in the prisoner’s dilemma on two-layer dynamic multiplex networks," Applied Mathematics and Computation, Elsevier, vol. 388(C).
    6. Juan A Lacomba & Francisco Lagos & Javier Perote, 2017. "The Lazarillo’s game: Sharing resources with asymmetric conditions," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-14, July.

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