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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2312.16815 |
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| _version_ | 1866909119642861568 |
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| author | Yuan, Bing Jiang, Zhang Lyu, Aobo Wu, Jiayun Wang, Zhipeng Yang, Mingzhe Liu, Kaiwei Mou, Muyun Cui, Peng |
| author_facet | Yuan, Bing Jiang, Zhang Lyu, Aobo Wu, Jiayun Wang, Zhipeng Yang, Mingzhe Liu, Kaiwei Mou, Muyun Cui, Peng |
| contents | Emergence and causality are two fundamental concepts for understanding complex systems. They are interconnected. On one hand, emergence refers to the phenomenon where macroscopic properties cannot be solely attributed to the cause of individual properties. On the other hand, causality can exhibit emergence, meaning that new causal laws may arise as we increase the level of abstraction. Causal emergence theory aims to bridge these two concepts and even employs measures of causality to quantify emergence. This paper provides a comprehensive review of recent advancements in quantitative theories and applications of causal emergence. Two key problems are addressed: quantifying causal emergence and identifying it in data. Addressing the latter requires the use of machine learning techniques, thus establishing a connection between causal emergence and artificial intelligence. We highlighted that the architectures used for identifying causal emergence are shared by causal representation learning, causal model abstraction, and world model-based reinforcement learning. Consequently, progress in any of these areas can benefit the others. Potential applications and future perspectives are also discussed in the final section of the review. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_16815 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies Yuan, Bing Jiang, Zhang Lyu, Aobo Wu, Jiayun Wang, Zhipeng Yang, Mingzhe Liu, Kaiwei Mou, Muyun Cui, Peng Physics and Society Artificial Intelligence Adaptation and Self-Organizing Systems 68P30 K.3.2 Emergence and causality are two fundamental concepts for understanding complex systems. They are interconnected. On one hand, emergence refers to the phenomenon where macroscopic properties cannot be solely attributed to the cause of individual properties. On the other hand, causality can exhibit emergence, meaning that new causal laws may arise as we increase the level of abstraction. Causal emergence theory aims to bridge these two concepts and even employs measures of causality to quantify emergence. This paper provides a comprehensive review of recent advancements in quantitative theories and applications of causal emergence. Two key problems are addressed: quantifying causal emergence and identifying it in data. Addressing the latter requires the use of machine learning techniques, thus establishing a connection between causal emergence and artificial intelligence. We highlighted that the architectures used for identifying causal emergence are shared by causal representation learning, causal model abstraction, and world model-based reinforcement learning. Consequently, progress in any of these areas can benefit the others. Potential applications and future perspectives are also discussed in the final section of the review. |
| title | Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies |
| topic | Physics and Society Artificial Intelligence Adaptation and Self-Organizing Systems 68P30 K.3.2 |
| url | https://arxiv.org/abs/2312.16815 |