دورية أكاديمية
Neural Information Squeezer for Causal Emergence
| العنوان: | Neural Information Squeezer for Causal Emergence |
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| المؤلفون: | Jiang Zhang, Kaiwei Liu |
| المصدر: | Entropy, Vol 25, Iss 1, p 26 (2022) |
| بيانات النشر: | MDPI AG, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Science LCC:Astrophysics LCC:Physics |
| مصطلحات موضوعية: | causal emergence, coarse-graining, invertible neural network, Science, Astrophysics, QB460-466, Physics, QC1-999 |
| الوصف: | Conventional studies of causal emergence have revealed that stronger causality can be obtained on the macro-level than the micro-level of the same Markovian dynamical systems if an appropriate coarse-graining strategy has been conducted on the micro-states. However, identifying this emergent causality from data is still a difficult problem that has not been solved because the appropriate coarse-graining strategy can not be found easily. This paper proposes a general machine learning framework called Neural Information Squeezer to automatically extract the effective coarse-graining strategy and the macro-level dynamics, as well as identify causal emergence directly from time series data. By using invertible neural network, we can decompose any coarse-graining strategy into two separate procedures: information conversion and information discarding. In this way, we can not only exactly control the width of the information channel, but also can derive some important properties analytically. We also show how our framework can extract the coarse-graining functions and the dynamics on different levels, as well as identify causal emergence from the data on several exampled systems. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 25010026 1099-4300 |
| Relation: | https://www.mdpi.com/1099-4300/25/1/26; https://doaj.org/toc/1099-4300 |
| DOI: | 10.3390/e25010026 |
| URL الوصول: | https://doaj.org/article/64e927994f3d48e8866f83cb85000399 |
| رقم الأكسشن: | edsdoj.64e927994f3d48e8866f83cb85000399 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 25010026 10994300 |
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| DOI: | 10.3390/e25010026 |