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(to be completed)

 


ThinkMind // International Journal On Advances in Systems and Measurements, volume 8, numbers 3 and 4, 2015 // View article sysmea_v8_n34_2015_10


Meta-Theorizing and Machine-Intelligent Modeling a Complex Adaptive System that is Poised for Resilience Using Architectural and Empirical Indicators

Authors:
Roberto Legaspi
Hiroshi Maruyama

Keywords: complex adaptive systems; intelligent systems; systems resilience

Abstract:
With our world witnessing critical systemic changes, we argue for a deeper understanding of what fundamentally constitutes and leads to critical system changes, and how the system can be resilient, i.e., persist in, adapt to, or transform from dramatically changing circumstances. We position our argument with long-standing theories on complexity, self-organization, criticality, chaos, and transformation, which are emergent properties shared by natural and physical complex systems for evolution and collapse. We further argue that there are system regimes that, although normally denote impending peril or eventual collapse, could actually push the system positively to be poised for resilience. In light of resilient systems, criticality and chaos can actually be leveraged by the system to promote adaptation or transformation that can lead to sustainability. Furthermore, our extensive simulation of complex adaptive system behaviors suggests that advantageous and deleterious system regimes can be predicted through architectural and empirical indicators. We framed our arguments in a two-fold complex systems resilience framework, i.e., with a meta-theory that integrates theories on complex system changes, and a machine-intelligent modeling task to infer from data the contextual behaviors of a resilient system.

Pages: 268 to 287

Copyright: Copyright (c) to authors, 2015. Used with permission.

Publication date: December 30, 2015

Published in: journal

ISSN: 1942-261x

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