计算机科学
随机图
小世界网络
无标度网络
网格
编队网络
网络结构
对抗制
复杂网络
相互依存的网络
比例(比率)
图形
网络模型
分布式计算
计算机安全
人工智能
理论计算机科学
数学
地理
地图学
几何学
万维网
作者
Hao Chen,Lin Wang,Xiaofan Wang
出处
期刊:Chaos
[American Institute of Physics]
日期:2023-03-01
卷期号:33 (3): 033123-033123
被引量:5
摘要
Inter-network combat and intra-network cooperation among structured systems are likely to have been recurrent features of human evolutionary history; however, little research has investigated the combat mechanism between structured systems that the adversarial interactions will cause the disability of agents and agents are prone to seek cooperation with neighbors. Hence, the current study has proposed a two-network combat game model and designed the corresponding rules of how to attack, how to be disabled, how to cooperate, and how to win. First, within the framework of our model, we have simulated the combat among four common network structures—the Erdős–Rényi (ER) random network, the grid graph, the small-world network, and the scale-free network. We found that the grid network always holds the highest winning percentage, while the ER random graph is most likely to lose when combating with the other three network structures. For each structure, we have also simulated the combat between the same network structures with different generating parameters. The simulations reveal that the small-world property and heterogeneity can promote winning a combat. Besides, by broadening and deepening cooperation, we have found that broader cooperation helps defeat the opposite system on grid and scale-free networks, yet hinders it on ER and Watts–Strogatz (WS) networks, while deeper cooperation can benefit to winning except on scale-free networks. These findings inform our understanding of the effects of structure and cooperation in a combat.
科研通智能强力驱动
Strongly Powered by AbleSci AI