序列时间网络上的进化动态
Anzhi Sheng1,2, Aming Li1,3, Long Wang1,3
1Center for Systems and Control, College of Engineering, Peking University, Beijing, China.
PLoS computational biology
|August 7, 2023
概括
网络中的人口增长促进了合作. 这项研究引入了顺序的时间网络来建模不断增长的人口,表明它们与静态结构相比增强了合作.
科学领域:
- 进化游戏理论的演化游戏理论.
- 网络科学 网络科学
- 计算生物学是一种计算生物学.
背景情况:
- 人口结构是合作进化的关键,但通常被认为是静态的.
- 现实世界的人口从小到大社区的动态增长.
- 静态模型无法捕捉这种动态增长,限制了进化见解.
研究的目的:
- 引入顺序时间网络来建模不断增长的人口.
- 将进化游戏理论扩展到动态网络结构和增长规则.
- 分析人口增长如何影响合作的演变.
主要方法:
- 在时间网络上开发了合作固定概率的分析规则.
- 研究中性漂移和弱选择场景.
- 为复杂的计算提出了一个平均场近似.
- 在经验数据集上使用数值模拟验证的结果.
主要成果:
- 与静态网络相比,连续时间网络可以增加合作固定的概率.
- 在中性漂移下,增长的效果取决于节点/边缘的增量.
- 在弱选择下,网络上的凝聚时间至关重要.
- 平均场近似准确地预测固定概率和临界比.
结论:
- 人口增长是现实世界进化系统的一个重要因素.
- 顺序时间网络为研究动态群体中的合作提供了一个强大的框架.
- 拟议的近似方法简化了复杂进化动态的分析.
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