EVOLUTIONARY ANALYSIS OF PRISONER’S DILEMMA GAMES BASED ON MIXED RANDOM-CONFORMITY SELECTING MODEL

Jianxia Wang (), Mengqi Hao (), Jinlong Ma and Sufeng Li ()
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Jianxia Wang: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
Mengqi Hao: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
Jinlong Ma: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
Sufeng Li: School of Economics, Hebei GEO University, Shijiazhuang 050031, P. R. China

Advances in Complex Systems (ACS), 2022, vol. 25, issue 07, 1-15

Abstract: Inspired by the conformity phenomenon in human society, we develop a mixed neighbor selecting model adopting random-conformity rule to explore the evolutionary weak prisoner’s dilemma game. The neighbor selection rule of nodes is adjusted based on their fitness and collective influence. Under the degree-normalized payoff framework, the findings derived from Monte Carlo simulations reveal that this mixed selecting model can contribute to an impressive improvement in the Barabási-Albert network’s cooperation. In addition, experimental data obtained by investigating the game-learning skeleton indicate that, in this mixed random-conformity selecting model, normalized collective influence at moderate depth length enables influential nodes to maintain a cooperative strategy for an extended period of time. This can promote the emergence of cooperative strategies at low-degree nodes by facilitating the formation of stable cooperation-clusters centered on high-degree nodes. In addition, the normalized collective influence at excessive depth length increases the likelihood that influential nodes become defectors, thereby inhibiting the growth of cooperation-clusters and limiting cooperation.

Keywords: Complex network; evolutionary game theory; neighbor selecting rule; collective influence; conformity effect (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0219525922500126

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