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复杂系统中的因果涌现研究综述

王志鹏 张江

王志鹏, 张江. 复杂系统中的因果涌现研究综述[J]. 北京师范大学学报(自然科学版), 2023, 59(5): 725-733. doi: 10.12202/j.0476-0301.2023049
引用本文: 王志鹏, 张江. 复杂系统中的因果涌现研究综述[J]. 北京师范大学学报(自然科学版), 2023, 59(5): 725-733. doi: 10.12202/j.0476-0301.2023049
WANG Zhipeng, ZHANG Jiang. Causal emergence in complex systems[J]. Journal of Beijing Normal University(Natural Science), 2023, 59(5): 725-733. doi: 10.12202/j.0476-0301.2023049
Citation: WANG Zhipeng, ZHANG Jiang. Causal emergence in complex systems[J]. Journal of Beijing Normal University(Natural Science), 2023, 59(5): 725-733. doi: 10.12202/j.0476-0301.2023049

复杂系统中的因果涌现研究综述

doi: 10.12202/j.0476-0301.2023049
基金项目: 国家自然科学基金资助项目(61673070)
详细信息
    通讯作者:

    张江(1978—),博士,教授,博士生导师. 研究方向:复杂系统建模、因果涌现、规模法则、机器学习. E-mail:zhangjiang@bnu.edu.cn

  • 中图分类号: N949

Causal emergence in complex systems

  • 摘要: 聚焦复杂系统:就涌现、因果以及因果涌现进行了定量描述;阐述了因果与涌现的联系;系统梳理了基于粗粒化和信息分解2种定量刻画因果涌现的方法,以及基于信息分解和神经信息压缩2种因果涌现辨识方法;详细介绍了各方法的基本原理、优缺点,以及相关应用等.基于粗粒化方法定义了因果涌现框架,其可应用于离散动力系统;利用信息分解方法求解时结果会依赖冗余信息;基于信息分解所提出的指标可识别数据中的因果涌现,且找到一个充分条件;基于数据驱动的神经信息压缩方法可扩展并应用于连续动力系统,并可自动提取不同层级的粗粒化函数,以及构建不同层级的动力学,还可识别不同类型动力学系统的因果涌现;改进现有方法并用于识别更为复杂的系统与学习自动化分组,解决通用动力学大模型等问题.

     

  • 图  1  抽象的因果涌现框架

    图  2  神经信息压缩方法的模型框架示意

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出版历程
  • 收稿日期:  2023-04-04
  • 录用日期:  2023-09-06
  • 网络出版日期:  2023-09-21
  • 刊出日期:  2023-10-31

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