Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference
In complex systems constrained by multiple factors, it is very important to identify the key influencing factors for mastering the evolution and development law of a system and for obtaining scientific decision-making suggestions or schemes. At present, the method based on experimental simulation is...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/19/10575 |
| _version_ | 1827722914106966016 |
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| author | Jianping Wu Yunjun Lu Dezhi Li Wenlu Zhou Jian Huang |
| author_facet | Jianping Wu Yunjun Lu Dezhi Li Wenlu Zhou Jian Huang |
| author_sort | Jianping Wu |
| collection | DOAJ |
| description | In complex systems constrained by multiple factors, it is very important to identify the key influencing factors for mastering the evolution and development law of a system and for obtaining scientific decision-making suggestions or schemes. At present, the method based on experimental simulation is limited by the difficulty of system model construction; DEMATEL (Factual Decision Trial and Evaluation Laboratory) is inevitably influenced by subjective factors. In view of this, we propose a novel model based on heuristic causal inference. By combining the network analysis in complex network science, the model defines the global/local causal pathway and the causal pathway’s length in the causal network and takes the causal pathway contribution degree as an indicator to measure the approximate causal effects. The model includes steps such as causal network learning, causal pathway contribution degree calculation, and key influencing factor identification. The model uses the Fast Causal Inference (FCI) algorithm with prior knowledge to learn the global causal network of the complex system and uses the heuristic causal inference to calculate the causal pathway contribution degree. The heuristic method draws on the idea of complex network topology analysis and measures the influence degree between variables by the number and distance of causal pathways. The key influencing factors are finally identified according to the causal pathway contribution degree. Based on the SECOM dataset, we carried out simulation experiments and demonstrated the feasibility and effectiveness of the proposed method. |
| first_indexed | 2024-03-10T21:49:57Z |
| format | Article |
| id | doaj.art-d4bcefb3f3f3427bbf0ea1dd37eef9ca |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T21:49:57Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d4bcefb3f3f3427bbf0ea1dd37eef9ca2023-11-19T14:01:17ZengMDPI AGApplied Sciences2076-34172023-09-0113191057510.3390/app131910575Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal InferenceJianping Wu0Yunjun Lu1Dezhi Li2Wenlu Zhou3Jian Huang4School of Information and Communication, National University of Defense Technology, Wuhan 430074, ChinaSchool of Information and Communication, National University of Defense Technology, Wuhan 430074, ChinaSchool of Information and Communication, National University of Defense Technology, Wuhan 430074, ChinaSchool of Information and Communication, National University of Defense Technology, Wuhan 430074, ChinaSchool of Information and Communication, National University of Defense Technology, Wuhan 430074, ChinaIn complex systems constrained by multiple factors, it is very important to identify the key influencing factors for mastering the evolution and development law of a system and for obtaining scientific decision-making suggestions or schemes. At present, the method based on experimental simulation is limited by the difficulty of system model construction; DEMATEL (Factual Decision Trial and Evaluation Laboratory) is inevitably influenced by subjective factors. In view of this, we propose a novel model based on heuristic causal inference. By combining the network analysis in complex network science, the model defines the global/local causal pathway and the causal pathway’s length in the causal network and takes the causal pathway contribution degree as an indicator to measure the approximate causal effects. The model includes steps such as causal network learning, causal pathway contribution degree calculation, and key influencing factor identification. The model uses the Fast Causal Inference (FCI) algorithm with prior knowledge to learn the global causal network of the complex system and uses the heuristic causal inference to calculate the causal pathway contribution degree. The heuristic method draws on the idea of complex network topology analysis and measures the influence degree between variables by the number and distance of causal pathways. The key influencing factors are finally identified according to the causal pathway contribution degree. Based on the SECOM dataset, we carried out simulation experiments and demonstrated the feasibility and effectiveness of the proposed method.https://www.mdpi.com/2076-3417/13/19/10575complex systemkey influencing factorscausal networkheuristic causal inferencecausal pathway contribution degree |
| spellingShingle | Jianping Wu Yunjun Lu Dezhi Li Wenlu Zhou Jian Huang Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference Applied Sciences complex system key influencing factors causal network heuristic causal inference causal pathway contribution degree |
| title | Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference |
| title_full | Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference |
| title_fullStr | Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference |
| title_full_unstemmed | Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference |
| title_short | Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference |
| title_sort | key influencing factors identification in complex systems based on heuristic causal inference |
| topic | complex system key influencing factors causal network heuristic causal inference causal pathway contribution degree |
| url | https://www.mdpi.com/2076-3417/13/19/10575 |
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