Home // ICONS 2015, The Tenth International Conference on Systems // View article
Authors:
Roberto Legaspi
Hiroshi Maruyama
Keywords: dynamic system theories; resilience theories; system evolution theories; intelligent systems
Abstract:
Resilience is the ability of a complex system to persist in, adapt to, or transform from dramatically changing circumstances. Our objective is to characterize the resilience of a complex system in depth by looking at what fundamentally constitutes and leads to system changes and how the system can be resilient to these changes. Our characterization is by a two-fold framework, i.e., with a meta-theory that integrates long-standing foundational theories of systemic change and a two-part machine-intelligent computational modeling, specifically, using network analysis and machine learning models, to realize our meta-theory. By starting with a meta-theory as background knowledge to guide our modeling, we avoid irrelevant, scattered and loosely knitted paradigms. Complementary, any truth presented by the inferred models that are not accommodated in the meta-theory may correct flaws in the meta-theory. To our knowledge, our framework that uses this linking of meta-theory and machine-intelligent modeling to characterize resilience is novel. The results we obtained from our simulations show that our framework is a systematic and pragmatic way of inferring predictive models of the contextual interaction behaviors of a resilient system.
Pages: 102 to 111
Copyright: Copyright (c) IARIA, 2015
Publication date: April 19, 2015
Published in: conference
ISSN: 2308-4243
ISBN: 978-1-61208-399-5
Location: Barcelona, Spain
Dates: from April 19, 2015 to April 24, 2015