[1] |
智韬,司光亚,贺筱媛. 电力关键基础设施网络仿真模型研究[J]. 系统仿真学报,2010,22(11):2732 doi: 10.16182/j.cnki.joss.2010.11.019
|
[2] |
叶青. 基于复杂网络理论的轨道交通网络脆弱性分析[J]. 中国安全科学学报,2012,22(2):122 doi: 10.3969/j.issn.1003-3033.2012.02.020
|
[3] |
KWAPIEŃ J,DROŻDŻ S. Physical approach to complex systems[J]. Physics Reports,2012,515(3/4):115
|
[4] |
王飞跃,史帝夫·兰森. 从人工生命到人工社会:复杂社会系统研究的现状和展望[J]. 复杂系统与复杂性科学,2004,1(1):33 doi: 10.3969/j.issn.1672-3813.2004.01.007
|
[5] |
王飞跃. 人工社会、计算实验、平行系统:关于复杂社会经济系统计算研究的讨论[J]. 复杂系统与复杂性科学,2004,1(4):25 doi: 10.3969/j.issn.1672-3813.2004.04.002
|
[6] |
彭雁虹,褚启勤,李怀祖. 生命系统理论及其应用综述[J]. 系统工程理论与实践,1997,17(3):35
|
[7] |
ANDERSON P W. More is different[J]. Science,1972,177(4047):393 doi: 10.1126/science.177.4047.393
|
[8] |
CAMAZINE S,DENEUBOURG J L,FRANKS N R,et al. Self-organization in biological systems[M]. New Jersey:Princeton University Press,2020
|
[9] |
苗东升. 论系统思维(六):重在把握系统的整体涌现性[J]. 系统科学学报,2006,14(1):1
|
[10] |
方锦清,汪小帆,刘曾荣. 略论复杂性问题和非线性复杂网络系统的研究[J]. 科技导报,2004,22(2):9 doi: 10.3321/j.issn:1000-7857.2004.02.003
|
[11] |
MEEHL P E,SELLARS W. The concept of emergence[J]. Minnesota Studies in the Philosophy of Science,1956,1:239
|
[12] |
BEDAU M A. Weak emergence[J]. Philosophical Perspectives,1997,11:375
|
[13] |
PEPPER S C. Emergence[J]. The Journal of Philosophy,1926,23(9):241 doi: 10.2307/2014779
|
[14] |
FROMM J. Types and forms of emergence[EB/OL]. (2005-06-13)[2023-02-15]. https://arxiv.org/abs/nlin/0506028.pdf
|
[15] |
PEARL J. Graphs,causality,and structural equation models[J]. Sociological Methods & Research,1998,27(2):226
|
[16] |
GRANGER C W J. Investigating causal relations by econometric models and cross-spectral methods[J]. Econometrica,1969,37(3):424 doi: 10.2307/1912791
|
[17] |
NEUBERG L G. Causality:models,reasoning,and inference,by Judea Pearl,Cambridge University Press,2000[J]. Econometric Theory,2003,19:675
|
[18] |
HOEL E P,ALBANTAKIS L,TONONI G. Quantifying causal emergence shows that macro can beat micro[J]. Proceedings of the National Academy of Sciences of the United States of America,2013,110(49):19790
|
[19] |
HOEL E. When the map is better than the territory[J]. Entropy,2017,19(5):188 doi: 10.3390/e19050188
|
[20] |
ROSAS F E,MEDIANO P A M,JENSEN H J,et al. Reconciling emergences:an information-theoretic approach to identify causal emergence in multivariate data[J]. PLoS Computational Biology,2020,16(12):e1008289 doi: 10.1371/journal.pcbi.1008289
|
[21] |
ZHANG J A,LIU K W. Neural information squeezer for causal emergence[J]. Entropy,2022,25(1):26 doi: 10.3390/e25010026
|
[22] |
MARSH H W,YEUNG A S. Top-down,bottom-up,and horizontal models:the direction of causality in multidimensional,hierarchical self-concept models[J]. Journal of Personality and Social Psychology,1998,75(2):509 doi: 10.1037/0022-3514.75.2.509
|
[23] |
CHVYKOV P,HOEL E. Causal geometry[J]. Entropy,2020,23(1):24 doi: 10.3390/e23010024
|
[24] |
VARLEY T,HOEL E. Emergence as the conversion of information:a unifying theory[EB/OL]. (2021-04-27)[2023-02-16].https://arxiv.org/abs/2104.13368.pdf
|
[25] |
KLEIN B,HOEL E. The emergence of informative higher scales in complex networks[J]. Complexity,2020,2020:1
|
[26] |
GRIEBENOW R,KLEIN B,HOEL E. Finding the right scale of a network:efficient identification of causal emergence through spectral clustering[EB/OL]. (2022-02-16)[2023-02-17]. https://arxiv.org/abs/1908.07565v1
|
[27] |
汪小帆,李翔,陈关荣. 复杂网络理论及其应用[M]. 北京:清华大学出版社,2006
|
[28] |
MARROW S,MICHAUD E J,HOEL E. Examining the causal structures of deep neural networks using information theory[J]. Entropy,2020,22(12):1429 doi: 10.3390/e22121429
|
[29] |
KLEIN B,HOEL E,SWAIN A,et al. Evolution and emergence:higher order information structure in protein interactomes across the tree of life[J]. Integrative Biology,2021,13(12):283 doi: 10.1093/intbio/zyab020
|
[30] |
BRENNAN K,ANSHUMAN S,TRAVIS B,et al. Exploring noise,degeneracy and determinism in biological networks with the einet package[J]. Methods in Ecology and Evolution,2022,13(4):799 doi: 10.1111/2041-210X.13805
|
[31] |
MEDIANO P A M,ROSAS F E,LUPPI A I,et al. Greater than the parts:a review of the information decomposition approach to causal emergence [EB/OL]. (2022-05-23)[2023-02-17]. https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2021.0246
|
[32] |
TONONI G,SPORNS O. Measuring information integration[J]. BMC Neuroscience,2003,4:31 doi: 10.1186/1471-2202-4-31
|
[33] |
WILLIAMS P L,BEER R D. Nonnegative decomposition of multivariate information[EB/OL]. (2010-04-14)[2023-02-18].https://arxiv.org/abs/1004.2515.pdf
|
[34] |
ROSAS F E,MEDIANO P A M,RASSOULI B,et al. An operational information decomposition via synergistic disclosure[J]. Journal of Physics A:Mathematical and Theoretical,2020,53(48):485001 doi: 10.1088/1751-8121/abb723
|
[35] |
REICHSTEIN M,CAMPS-VALLS G,STEVENS B,et al. Deep learning and process understanding for data-driven Earth system science[J]. Nature,2019,566(7743):195 doi: 10.1038/s41586-019-0912-1
|
[36] |
SENIOR A W,EVANS R,JUMPER J,et al. Improved protein structure prediction using potentials from deep learning[J]. Nature,2020,577(7792):706 doi: 10.1038/s41586-019-1923-7
|
[37] |
KEISER M J,SETOLA V,IRWIN J J,et al. Predicting new molecular targets for known drugs[J]. Nature,2009,462(7270):175 doi: 10.1038/nature08506
|
[38] |
程乐峰,余涛,张孝顺,等. 机器学习在能源与电力系统领域的应用和展望[J]. 电力系统自动化,2019,43(1):15
|
[39] |
张伟伟,朱林阳,刘溢浪,等. 机器学习在湍流模型构建中的应用进展[J]. 空气动力学学报,2019,37(3):444
|
[40] |
GLYMOUR C,ZHANG K,SPIRTES P. Review of causal discovery methods based on graphical models[EB/OL]. (2019-06-04)[2023-02-18]. https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2021.0246
|
[41] |
CASADIEGO J,NITZAN M,HALLERBERG S,et al. Model-free inference of direct network interactions from nonlinear collective dynamics[J]. Nature Communications,2017,8:2192 doi: 10.1038/s41467-017-02288-4
|
[42] |
ZHANG Y,GUO Y,ZHANG Z,et al. Universal framework for reconstructing complex networks and node dynamics from discrete or continuous dynamics data[EB/OL]. (2022-09-16)[2023-02-18]. https://journals.aps.org/pre/abstract/10.1103/PhysRevE.106.034315
|
[43] |
GLYMOUR C,ZHANG K,SPIRTES P. Review of causal discovery methods based on graphical models[J]. Frontiers in Genetics,2019,10:524 doi: 10.3389/fgene.2019.00524
|
[44] |
蔡瑞初,陈薇,张坤,等. 基于非时序观察数据的因果关系发现综述[J]. 计算机学报,2017,40(6):1470 doi: 10.11897/SP.J.1016.2017.01470
|
[45] |
HU H Y,LI S H,WANG L,et al. Machine learning holographic mapping by neural network renormalization group[J]. Physical Review Research,2020,2(2):023369 doi: 10.1103/PhysRevResearch.2.023369
|
[46] |
LI S H,WANG L. Neural network renormalization group[J]. Physical Review Letters,2018,121(26):260601 doi: 10.1103/PhysRevLett.121.260601
|
[47] |
DINH L,SOHL-DICKSTEIN J,BENGIO S. Density estimation using Real NVP[EB/OL]. (2017-02-27)[2023-02-18].https://arxiv.org/abs/1605.08803
|
[48] |
HOEL E,LEVIN M. Emergence of informative higher scales in biological systems:a computational toolkit for optimal prediction and control[J]. Communicative & Integrative Biology,2020,13(1):108
|
[49] |
SWAIN A,WILLIAMS S D,DI FELICE L J,et al. Interactions and information:exploring task allocation in ant colonies using network analysis[J]. Animal Behaviour,2022,189:69 doi: 10.1016/j.anbehav.2022.04.015
|
[50] |
SCHÖLKOPF B,LOCATELLO F,BAUER S,et al. Toward causal representation learning[J]. Proceedings of the IEEE,2021,109(5):612 doi: 10.1109/JPROC.2021.3058954
|
[51] |
IWASAKI Y,SIMON H A. Causality and model abstraction[J]. Artificial Intelligence,1994,67(1):143 doi: 10.1016/0004-3702(94)90014-0
|
[52] |
MATSUO Y,LECUN Y,SAHANI M,et al. Deep learning,reinforcement learning,and world models[J]. Neural Networks,2022,152:267 doi: 10.1016/j.neunet.2022.03.037
|