Finding emergence in data by maximizing effective information  

作　　者：Mingzhe Yang Zhipeng Wang Kaiwei Liu Yingqi Rong Bing Yuan Jiang Zhang 

机构地区：[1]School of Systems Science,Beijing Normal University,Beijing 100875,China [2]Department of Cognitive Science,Johns Hopkins University,Baltimore 21218,USA [3]Swarma Research,Beijing 102300,China

出　　处：《National Science Review》2025年第1期259-271,共13页国家科学评论(英文版)

基　　金：supported in part by the Interdisciplinary Research Foundation for Doctoral Candidates of Beijing Normal University(BNUXKJC2219).

摘　　要：Quantifying emergence and modeling emergent dynamics in a data-driven manner for complex dynamical systems is challenging due to the fact that emergent behaviors cannot be directly captured by micro-level observational data.Thus,it is crucial to develop a framework to identify emergent phenomena and capture emergent dynamics at the macro-level using available data.Inspired by the theory of causal emergence(CE),this paper introduces a machine learning framework to learn macro-dynamics in an emergent latent space and quantify the degree of CE.The framework maximizes effective information,resulting in a macro-dynamics model with enhanced causal effects.Experimental results on simulated and real data demonstrate the effectiveness of the proposed framework.It quantifies degrees of CE effectively under various conditions and reveals distinct influences of different noise types.It can learn a one-dimensional coarse-grained macro-state from functional magnetic resonance imaging data to represent complex neural activities during movie clip viewing.Furthermore,improved generalization to different test environments is observed across all simulation data.

关 键 词：causal emergence dynamics learning effective information coarse graining invertible neural network 

分 类 号：X321[环境科学与工程—环境工程]