接近爆炸性同步决定了神经和经济危机中的网络崩和恢复轨迹
UnCheol Lee1, Hyoungkyu Kim2, Minkyung Kim2
1Department of Anesthesiology, Center for Consciousness Science, Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, Michigan, USA.
bioRxiv : the preprint server for biology
|December 16, 2024
概括
复杂系统在偏离关键时失去最佳功能. 接近爆炸性同步 (ES) 决定了系统在干扰后如何快速失去和恢复关键性,从而可以预测恢复模式.
科学领域:
- 复杂系统科学 复杂系统科学
- 网络理论 网络理论
- 统计物理学的统计物理.
背景情况:
- 复杂的系统需要在秩序和混乱 (关键性) 之间保持平衡,以实现最佳功能.
- 偏离临界值可能会导致系统损坏,异常快速或缓慢的过渡会带来重大风险.
- 推动这些快速或延迟的关键转型的基本机制仍然不太清楚.
研究的目的:
- 调查网络接近爆炸性同步 (ES) 在确定关键性损失和恢复动态方面的作用.
- 从实证数据中开发一种推断网络相位过渡类型和预测关键性过渡模式的方法.
- 展示这些预测在现实世界复杂系统中的实际应用.
主要方法:
- 复杂网络的计算建模以模拟关键性动态.
- 基于接近爆炸同步 (ES) 的特征动态分析.
- 使用从人类大脑 (麻醉下) 和金融市场 (经济危机期间) 的经验数据进行验证.
主要成果:
- 网络靠近ES显著影响了关键性损失和恢复的速度和轨迹.
- 计算模型成功预测了基于网络动态的关键性过渡模式.
- 来自人类大脑和股票市场的经验数据验证了早期和长期康复的预测能力.
结论:
- 接近爆炸性同步是复杂系统弹性和干扰后恢复的关键决定因素.
- 这项研究为预测系统行为和设计更强大的网络提供了一个框架.
- 了解这些转变动态对管理各种领域的复杂系统有影响,从神经科学到经济学.
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