爆发列车在事件序列中的等级组织
Takayuki Hiraoka1, Hang-Hyun Jo2
1Department of Computer Science, Aalto University, Espoo 00076, Finland.
Chaos (Woodbury, N.Y.)
|November 11, 2025
概括
复杂的事件序列表现出跨时间尺度的等级结构. 这项研究揭示了爆炸式列车大小中的权力法分配,这表明自然和社会过程的底层层次组织.
科学领域:
- 复杂系统科学 复杂系统科学
- 统计物理 统计物理
- 网络科学 网络科学
背景情况:
- 许多自然和社会过程产生离散的事件序列.
- 这些序列往往偏离简单的波桑过程,表现出重尾间事件时间分布.
- 此外,在这些过程中,爆发列车的大小显示出沉重的尾巴,表明了超出简单间事件时间的复杂时间相关性.
研究的目的:
- 分析经验事件序列,并识别跨不同时间尺度的等级结构.
- 在复杂的时间过程中调查爆发列车的统计性质.
- 提出一种能够复制观察到的等级结构的动态模型.
主要方法:
- 分析来自不同领域的经验事件序列.
- 对事件间时间和火车尺寸分布的统计分析.
- 为不同的时间尺度开发和应用具有独立记忆过程的动态算法.
主要成果:
- 经验事件序列展示了跨多个时间尺度的层次组织.
- 构成较长时间段列车的较短时间段爆发列车的数量遵循权力法分布.
- 拟议的动态算法成功生成了与这些观察到的等级结构的事件序列.
结论:
- 复杂的时间过程表现出层次结构,在不同时间尺度上以火车大小爆发.
- 这些层次结构的特点是权力法关系,这表明了基本的组织原则.
- 具有多时间尺度记忆过程的动态模型可以有效地捕捉这些复杂的时间动态.
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