Evolutionary dynamics of strategies for threshold snowdrift games on complex networks

被引:24
|
作者
Zhu, Yuying [1 ,2 ]
Zhang, Jianlei [1 ,2 ]
Sun, Qinglin [1 ,2 ]
Chen, Zengqiang [1 ,2 ]
机构
[1] Nankai Univ, Dept Automat, Coll Comp & Control Engn, Tianjin 300071, Peoples R China
[2] Nankai Univ, Tianjin Key Lab Intelligent Robot, Tianjin 300071, Peoples R China
基金
中国国家自然科学基金;
关键词
Dynamics of social systems; Evolutionary game theory; Cooperation; PUBLIC-GOODS GAMES; PRISONERS-DILEMMA; GROUP-SIZE; COOPERATION; PUNISHMENT; REPUTATION; DIVERSITY; EMERGENCE; BEHAVIOR; PROMOTES;
D O I
10.1016/j.knosys.2017.05.016
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The puzzle of altruistic cooperation attracts many concerns of researchers in multiple subjects nowadays. In this work we establish punishment in the framework of a threshold multiple-player snowdrift game employed as the scenario for the cooperative dilemma problem. We show by analysis that given this assumption, punishing free riders can significantly influence the evolution dynamics, and the results are driven by the specific components of the punishing rule. Intriguingly larger thresholds of the game provide a more favorable scenario for the coexistence of the cooperators and defectors under a broad value range of parameters. Furthermore, we provide a two-layer network framework for describing the individual interactions, by extending the threshold snowdrift games to the double-layers networks. Here, cooperators are best supported on complex networks by applying the threshold, moreover, the interlinks between the two layers are conducive to reinforce the network reciprocity. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:51 / 61
页数:11
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