4.7 Article

Evolutionary multigame with conformists and profiteers based on dynamic complex networks

期刊

CHAOS
卷 32, 期 2, 页码 -

出版社

AIP Publishing
DOI: 10.1063/5.0081954

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资金

  1. Ministry of Education in China (MOE) Project of Humanities and Social Sciences [21YJCZH028]
  2. Ministry of Science and Higher Education of the Russian Federation [075-15-2020-926]

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Evolutionary game on complex networks offers a fresh perspective for examining and predicting group decision-making behavior in interactive environments. This study investigates the impact of players' conformity and profit-seeking tendencies on the evolution of cooperation in multiple games. By introducing player similarity and considering the influence of multiple games, the researchers propose two different strategy-updating rules and explore the evolutionary process, including strategy updating, type transformation, and dynamic evolution of the network structure. The simulation results on scale-free and regular networks support the validity of the proposed model, offering insights into strategy transition, type transition, and network topology properties.
Evolutionary game on complex networks provides a new research framework for analyzing and predicting group decision-making behavior in an interactive environment, in which most researchers assumed players as profiteers. However, current studies have shown that players are sometimes conformists rather than profit-seeking in society, but most research has been discussed on a simple game without considering the impact of multiple games. In this paper, we study the influence of conformists and profiteers on the evolution of cooperation in multiple games and illustrate two different strategy-updating rules based on these conformists and profiteers. Different from previous studies, we introduce a similarity between players into strategy-updating rules and explore the evolutionary game process, including the strategy updating, the transformation of players' type, and the dynamic evolution of the network structure. In the simulation, we implement our model on scale-free and regular networks and provide some explanations from the perspective of strategy transition, type transition, and network topology properties to prove the validity of our model.

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