在具有合作和竞争动态的适应性简化复合体中的同步过渡
S Nirmala Jenifer1, Dibakar Ghosh2, Paulsamy Muruganandam1
1Department of Physics, Bharathidasan University, Tiruchirappalli 620024, Tamil Nadu, India.
Chaos (Woodbury, N.Y.)
|December 17, 2024
概括
本研究引入了一种新的适应性网络模型,该模型包括高阶相互作用和多种适应类型. 这项研究揭示了这些因素如何影响大脑和社交网络等复杂系统中的同步过渡.
科学领域:
- 复杂的系统复杂的系统.
- 网络科学 网络科学
- 统计物理学的统计物理.
背景情况:
- 当前的适应性网络模型往往忽视了更高层次的相互作用和各种适应类型 (合作/竞争).
- 这些被忽视的因素在现实世界的系统中至关重要,例如人类大脑和社交网络.
- 了解这些动态是解释集体行为,如同步的关键.
研究的目的:
- 开发一个适应性网络模型,包括更高层次的交互和合作和竞争性适应.
- 研究这些组合因素对集体性质的影响,特别是同步相转换.
- 探索网络结构和适应类型如何决定同步的方式和时间.
主要方法:
- 开发一个简化的自适应网络模型.
- 分析相位转换,专注于同步现象.
- 研究更高阶相互作用和合强度对系统动态的影响.
- 检查只有竞争性适应的系统与结合合作和竞争性适应的系统.
主要成果:
- 高阶相互作用可以在竞争性适应下将同步从第一阶转移到第二阶.
- 即使没有对互动,如果高阶合足够强,同步也是可能的.
- 竞争性适应导致类似二次阶段过渡和集群.
- 合并的合作和竞争性适应导致具有歇斯底里的第一阶段类似阶段过渡.
结论:
- 该模型成功地捕捉了由高阶相互作用和各种适应类型影响的复杂同步动态.
- 这些发现表明,网络结构和适应机制可以调整以控制同步开始和行为.
- 这项工作为理解适应性系统中的集体现象提供了更全面的框架.
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