Bundled Causal History Interaction
Complex systems arise as a result of the nonlinear interactions between components. In particular, the evolutionary dynamics of a multivariate system encodes the ways in which different variables interact with each other individually or in groups. One fundamental question that remains unanswered is:...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/22/3/360 |
| _version_ | 1798025579222532096 |
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| author | Peishi Jiang Praveen Kumar |
| author_facet | Peishi Jiang Praveen Kumar |
| author_sort | Peishi Jiang |
| collection | DOAJ |
| description | Complex systems arise as a result of the nonlinear interactions between components. In particular, the evolutionary dynamics of a multivariate system encodes the ways in which different variables interact with each other individually or in groups. One fundamental question that remains unanswered is: How do two non-overlapping multivariate subsets of variables interact to causally determine the outcome of a specific variable? Here, we provide an information-based approach to address this problem. We delineate the temporal interactions between the bundles in a probabilistic graphical model. The strength of the interactions, captured by partial information decomposition, then exposes complex behavior of dependencies and memory within the system. The proposed approach successfully illustrated complex dependence between cations and anions as determinants of <i>pH</i> in an observed stream chemistry system. In the studied catchment, the dynamics of <i>pH</i> is a result of both cations and anions through mainly synergistic effects of the two and their individual influences as well. This example demonstrates the potentially broad applicability of the approach, establishing the foundation to study the interaction between groups of variables in a range of complex systems. |
| first_indexed | 2024-04-11T18:21:07Z |
| format | Article |
| id | doaj.art-9c672c776c3e4b589eb56647302c06a0 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T18:21:07Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-9c672c776c3e4b589eb56647302c06a02022-12-22T04:09:45ZengMDPI AGEntropy1099-43002020-03-0122336010.3390/e22030360e22030360Bundled Causal History InteractionPeishi Jiang0Praveen Kumar1Ven Te Chow Hydrosystem Laboratory, Civil and Environmental Engineering, University of Illinois, Urbana, IL 61801, USAVen Te Chow Hydrosystem Laboratory, Civil and Environmental Engineering, University of Illinois, Urbana, IL 61801, USAComplex systems arise as a result of the nonlinear interactions between components. In particular, the evolutionary dynamics of a multivariate system encodes the ways in which different variables interact with each other individually or in groups. One fundamental question that remains unanswered is: How do two non-overlapping multivariate subsets of variables interact to causally determine the outcome of a specific variable? Here, we provide an information-based approach to address this problem. We delineate the temporal interactions between the bundles in a probabilistic graphical model. The strength of the interactions, captured by partial information decomposition, then exposes complex behavior of dependencies and memory within the system. The proposed approach successfully illustrated complex dependence between cations and anions as determinants of <i>pH</i> in an observed stream chemistry system. In the studied catchment, the dynamics of <i>pH</i> is a result of both cations and anions through mainly synergistic effects of the two and their individual influences as well. This example demonstrates the potentially broad applicability of the approach, establishing the foundation to study the interaction between groups of variables in a range of complex systems.https://www.mdpi.com/1099-4300/22/3/360bundled causal dynamicsinformation measurescomplex system |
| spellingShingle | Peishi Jiang Praveen Kumar Bundled Causal History Interaction Entropy bundled causal dynamics information measures complex system |
| title | Bundled Causal History Interaction |
| title_full | Bundled Causal History Interaction |
| title_fullStr | Bundled Causal History Interaction |
| title_full_unstemmed | Bundled Causal History Interaction |
| title_short | Bundled Causal History Interaction |
| title_sort | bundled causal history interaction |
| topic | bundled causal dynamics information measures complex system |
| url | https://www.mdpi.com/1099-4300/22/3/360 |
| work_keys_str_mv | AT peishijiang bundledcausalhistoryinteraction AT praveenkumar bundledcausalhistoryinteraction |