在新兴情报中复杂性同步.
Korosh Mahmoodi1, Scott E Kerick2, Piotr J Franaszczuk2,3
1US Combat Capabilities Command, Army Research Laboratory, Aberdeen Proving Ground, MD, 21005, USA. koroshmahmoodi@gmail.com.
Scientific reports
|March 22, 2024
概括
这项研究揭示了使用基于多代理模型的社交网络中的复杂性同步 (CS). 这一发现表明,CS是跨生物和社会系统的基本属性.
科学领域:
- 复杂系统科学 复杂系统科学
- 计算社会科学 计算社会科学
- 网络科学 网络科学
背景情况:
- 复杂性同步 (CS) 是一种在生物器官网络 (ON) 中观察到的现象,例如大脑,肺和心脏.
- 从理论上讲,CS可以通过同步多分形维度 (MFD) 缩放参数在同时测量的时间序列中进行解释.
- 之前的研究在神经生理学,呼吸和心血管反应方面建立了CS.
研究的目的:
- 在一个模拟的社交网络中调查复杂性同步 (CS) 的出现.
- 确定CS是否可以在没有外部控制的情况下从自我组织的相互作用中产生.
- 探索CS在社会现象和人机交互中的潜力.
主要方法:
- 使用基于多个代理的模型 (MABM) 与自私算法 (SA) 代理来模拟社交网络.
- 使用修改的扩散分析 (DEA) 来检测复杂性同步 (CS).
- 模拟两组参与反协调游戏的代理人之间的偏见自利.
主要成果:
- 在社会网络模型中的自我组织群体的新兴智能中观察到复杂性同步 (CS).
- 证明了CS源于代理人之间的相互适应性相互作用,即使有偏见的私利.
- 模拟成功复制了类似于生物器官网络 (ONs) 中发现的CS模式.
结论:
- 复杂性同步 (CS) 是自我组织的社会相互作用的可信的新兴属性.
- 这些发现表明,CS是一个统一的原则,适用于生物系统和人工/社会网络.
- 这项研究支持CS在理解现实世界的社会动态和人机系统方面的潜力.
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