Automatic Emergence Detection in Complex Systems
Complex systems consist of multiple interacting subsystems, whose nonlinear interactions can result in unanticipated (emergent) system events. Extant systems analysis approaches fail to detect such emergent properties, since they analyze each subsystem separately and arrive at decisions typically th...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/3460919 |
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| _version_ | 1832557037990445056 |
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| author | Eugene Santos Yan Zhao |
| author_facet | Eugene Santos Yan Zhao |
| author_sort | Eugene Santos |
| collection | DOAJ |
| description | Complex systems consist of multiple interacting subsystems, whose nonlinear interactions can result in unanticipated (emergent) system events. Extant systems analysis approaches fail to detect such emergent properties, since they analyze each subsystem separately and arrive at decisions typically through linear aggregations of individual analysis results. In this paper, we propose a quantitative definition of emergence for complex systems. We also propose a framework to detect emergent properties given observations of its subsystems. This framework, based on a probabilistic graphical model called Bayesian Knowledge Bases (BKBs), learns individual subsystem dynamics from data, probabilistically and structurally fuses said dynamics into a single complex system dynamics, and detects emergent properties. Fusion is the central element of our approach to account for situations when a common variable may have different probabilistic distributions in different subsystems. We evaluate our detection performance against a baseline approach (Bayesian Network ensemble) on synthetic testbeds from UCI datasets. To do so, we also introduce a method to simulate and a metric to measure discrepancies that occur with shared/common variables. Experiments demonstrate that our framework outperforms the baseline. In addition, we demonstrate that this framework has uniform polynomial time complexity across all three learning, fusion, and reasoning procedures. |
| format | Article |
| id | doaj-art-8ceec7ee0ca646bf8c80b254a4e68f70 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-8ceec7ee0ca646bf8c80b254a4e68f702025-02-03T05:43:52ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/34609193460919Automatic Emergence Detection in Complex SystemsEugene Santos0Yan Zhao1Thayer School of Engineering, Dartmouth College, Hanover, NH, USAThayer School of Engineering, Dartmouth College, Hanover, NH, USAComplex systems consist of multiple interacting subsystems, whose nonlinear interactions can result in unanticipated (emergent) system events. Extant systems analysis approaches fail to detect such emergent properties, since they analyze each subsystem separately and arrive at decisions typically through linear aggregations of individual analysis results. In this paper, we propose a quantitative definition of emergence for complex systems. We also propose a framework to detect emergent properties given observations of its subsystems. This framework, based on a probabilistic graphical model called Bayesian Knowledge Bases (BKBs), learns individual subsystem dynamics from data, probabilistically and structurally fuses said dynamics into a single complex system dynamics, and detects emergent properties. Fusion is the central element of our approach to account for situations when a common variable may have different probabilistic distributions in different subsystems. We evaluate our detection performance against a baseline approach (Bayesian Network ensemble) on synthetic testbeds from UCI datasets. To do so, we also introduce a method to simulate and a metric to measure discrepancies that occur with shared/common variables. Experiments demonstrate that our framework outperforms the baseline. In addition, we demonstrate that this framework has uniform polynomial time complexity across all three learning, fusion, and reasoning procedures.http://dx.doi.org/10.1155/2017/3460919 |
| spellingShingle | Eugene Santos Yan Zhao Automatic Emergence Detection in Complex Systems Complexity |
| title | Automatic Emergence Detection in Complex Systems |
| title_full | Automatic Emergence Detection in Complex Systems |
| title_fullStr | Automatic Emergence Detection in Complex Systems |
| title_full_unstemmed | Automatic Emergence Detection in Complex Systems |
| title_short | Automatic Emergence Detection in Complex Systems |
| title_sort | automatic emergence detection in complex systems |
| url | http://dx.doi.org/10.1155/2017/3460919 |
| work_keys_str_mv | AT eugenesantos automaticemergencedetectionincomplexsystems AT yanzhao automaticemergencedetectionincomplexsystems |