Causality and Complex Systems
Complex systems are unified wholes formed by many interacting units. One key source of their complexity lies in the intricate entanglement of causal structures. A notable feature of these systems is the phenomenon of emergent causality, where stronger causal relationships may arise at the macro-scal...
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| Materialtyp: | Online |
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| Språk: | engelska |
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MDPI - Multidisciplinary Digital Publishing Institute
2026
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| Länkar: | ONIX_20260416T142754_9783725867905_34 |
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| _version_ | 1869519655012401152 |
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| collection | Directory of Open Access Books |
| description | Complex systems are unified wholes formed by many interacting units. One key source of their complexity lies in the intricate entanglement of causal structures. A notable feature of these systems is the phenomenon of emergent causality, where stronger causal relationships may arise at the macro-scale rather than the micro-scale, as seen in fields like statistical mechanics. This Special Issue focuses on "Causality and Complex Systems", examining the interplay of causal relationships and the emergence of causal structures within complex systems. The 17 articles in this issue encompass a broad spectrum of topics, ranging from theoretical frameworks to practical applications, with the shared goal of advancing our understanding of causality in dynamic, interconnected systems.Key contributions of this issue include novel theoretical frameworks for quantifying causal emergence, innovative causal machine learning algorithms, and information-theoretic measures for analyzing causality. Methodological advancements are also presented for causal discovery in complex and nonlinear systems. Furthermore, this issue features interdisciplinary applications across neuroscience, biology, sociology, and environmental science, demonstrating the versatility of causal methods in addressing real-world phenomena. Ultimately, this Special Issue enhances both the theoretical understanding of causality in complex systems and provides practical tools and methodologies to tackle challenges in data-driven research. |
| format | Online |
| id | doab-20.500.12854ir-175429 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1754292026-04-16T20:51:24Z Causality and Complex Systems Zhang, Jiang Cui, Peng Zenil, Hector Causal emergence Coarse-graining Invertible neural network Emergence Causality Higher-order interaction Partial information decomposition Synergy Networks Neuroscience Information geometry Transfer Entropy Granger Causality Information causal rate Signal processing Nonlinear models Non-stationary Probability distribution Environmental factors Respiratory infection Nonlinear system Kernel Granger causality Effective network Schizophrenia MEG Nonequilibrium Complexity Causal analysis Granger causality Bootstrap methods Multivariate time series Impulse response function Aristotle Causation Causal closure Downwards causation N A Causal representation learning Video prediction Transfer learning Few-shot learning Causal discovery Independence tests Functional data analysis Kernel methods Direction of information flow Transfer entropy Anticipatory dynamics Zebrafish Causal inference Maximizing mutual information Unobserved causes Complex system Causal emergence identification Effective information Machine learning Linear stochastic iteration system Berkson’s paradox Multivariate information theory Higher-order interactions Model-free Data-driven Nonlinear dynamics Team dynamics Information theory Graph theory Football analytics Collective behavior thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Complex systems are unified wholes formed by many interacting units. One key source of their complexity lies in the intricate entanglement of causal structures. A notable feature of these systems is the phenomenon of emergent causality, where stronger causal relationships may arise at the macro-scale rather than the micro-scale, as seen in fields like statistical mechanics. This Special Issue focuses on "Causality and Complex Systems", examining the interplay of causal relationships and the emergence of causal structures within complex systems. The 17 articles in this issue encompass a broad spectrum of topics, ranging from theoretical frameworks to practical applications, with the shared goal of advancing our understanding of causality in dynamic, interconnected systems.Key contributions of this issue include novel theoretical frameworks for quantifying causal emergence, innovative causal machine learning algorithms, and information-theoretic measures for analyzing causality. Methodological advancements are also presented for causal discovery in complex and nonlinear systems. Furthermore, this issue features interdisciplinary applications across neuroscience, biology, sociology, and environmental science, demonstrating the versatility of causal methods in addressing real-world phenomena. Ultimately, this Special Issue enhances both the theoretical understanding of causality in complex systems and provides practical tools and methodologies to tackle challenges in data-driven research. 2026-04-16T20:51:16Z 2026-04-16T20:51:16Z 2026 book ONIX_20260416T142754_9783725867905_34 9783725867905 9783725867912 https://directory.doabooks.org/handle/20.500.12854/175429 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/ https://mdpi.com/books/pdfview/book/12348 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-6791-2 10.3390/books978-3-7258-6791-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725867905 9783725867912 410 CH open access |
| spellingShingle | Causal emergence Coarse-graining Invertible neural network Emergence Causality Higher-order interaction Partial information decomposition Synergy Networks Neuroscience Information geometry Transfer Entropy Granger Causality Information causal rate Signal processing Nonlinear models Non-stationary Probability distribution Environmental factors Respiratory infection Nonlinear system Kernel Granger causality Effective network Schizophrenia MEG Nonequilibrium Complexity Causal analysis Granger causality Bootstrap methods Multivariate time series Impulse response function Aristotle Causation Causal closure Downwards causation N A Causal representation learning Video prediction Transfer learning Few-shot learning Causal discovery Independence tests Functional data analysis Kernel methods Direction of information flow Transfer entropy Anticipatory dynamics Zebrafish Causal inference Maximizing mutual information Unobserved causes Complex system Causal emergence identification Effective information Machine learning Linear stochastic iteration system Berkson’s paradox Multivariate information theory Higher-order interactions Model-free Data-driven Nonlinear dynamics Team dynamics Information theory Graph theory Football analytics Collective behavior thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Causality and Complex Systems |
| title | Causality and Complex Systems |
| title_full | Causality and Complex Systems |
| title_fullStr | Causality and Complex Systems |
| title_full_unstemmed | Causality and Complex Systems |
| title_short | Causality and Complex Systems |
| title_sort | causality and complex systems |
| topic | Causal emergence Coarse-graining Invertible neural network Emergence Causality Higher-order interaction Partial information decomposition Synergy Networks Neuroscience Information geometry Transfer Entropy Granger Causality Information causal rate Signal processing Nonlinear models Non-stationary Probability distribution Environmental factors Respiratory infection Nonlinear system Kernel Granger causality Effective network Schizophrenia MEG Nonequilibrium Complexity Causal analysis Granger causality Bootstrap methods Multivariate time series Impulse response function Aristotle Causation Causal closure Downwards causation N A Causal representation learning Video prediction Transfer learning Few-shot learning Causal discovery Independence tests Functional data analysis Kernel methods Direction of information flow Transfer entropy Anticipatory dynamics Zebrafish Causal inference Maximizing mutual information Unobserved causes Complex system Causal emergence identification Effective information Machine learning Linear stochastic iteration system Berkson’s paradox Multivariate information theory Higher-order interactions Model-free Data-driven Nonlinear dynamics Team dynamics Information theory Graph theory Football analytics Collective behavior thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| topic_facet | Causal emergence Coarse-graining Invertible neural network Emergence Causality Higher-order interaction Partial information decomposition Synergy Networks Neuroscience Information geometry Transfer Entropy Granger Causality Information causal rate Signal processing Nonlinear models Non-stationary Probability distribution Environmental factors Respiratory infection Nonlinear system Kernel Granger causality Effective network Schizophrenia MEG Nonequilibrium Complexity Causal analysis Granger causality Bootstrap methods Multivariate time series Impulse response function Aristotle Causation Causal closure Downwards causation N A Causal representation learning Video prediction Transfer learning Few-shot learning Causal discovery Independence tests Functional data analysis Kernel methods Direction of information flow Transfer entropy Anticipatory dynamics Zebrafish Causal inference Maximizing mutual information Unobserved causes Complex system Causal emergence identification Effective information Machine learning Linear stochastic iteration system Berkson’s paradox Multivariate information theory Higher-order interactions Model-free Data-driven Nonlinear dynamics Team dynamics Information theory Graph theory Football analytics Collective behavior thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| url | ONIX_20260416T142754_9783725867905_34 |