Automatic Emergence Detection in Complex Systems
Joint Authors
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-24, 24 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-09-12
Country of Publication
Egypt
No. of Pages
24
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Santos, Eugene& Zhao, Yan. 2017. Automatic Emergence Detection in Complex Systems. Complexity،Vol. 2017, no. 2017, pp.1-24.
https://search.emarefa.net/detail/BIM-1142724
Modern Language Association (MLA)
Santos, Eugene& Zhao, Yan. Automatic Emergence Detection in Complex Systems. Complexity No. 2017 (2017), pp.1-24.
https://search.emarefa.net/detail/BIM-1142724
American Medical Association (AMA)
Santos, Eugene& Zhao, Yan. Automatic Emergence Detection in Complex Systems. Complexity. 2017. Vol. 2017, no. 2017, pp.1-24.
https://search.emarefa.net/detail/BIM-1142724
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142724