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TC 5.4. Large Scale Complex Systems

Archilla, J. Ed , Palmero, F. Ed , Lemos, M. Ed , Casado-Pascual, J. Available Formats: Hardcover eBook Softcover. Carmona, V. Ed , Cuevas-Maraver, J. Edelman, M. Ed , Macau, E. Ed , Sanjuan, M. Edmonds, B. Ed , Meyer, R. Ed , Tsasis, P. Hofrichter, J. Publishing With Us. Book Authors Journal Authors.

Understanding Complex Systems. Such systems are complex in both their composition — typically many different kinds of components interacting simultaneously and nonlinearly with each other and their environments on multiple levels — and in the rich diversity of behavior of which they are capable. UCS is explicitly transdisciplinary.

What is a Complex System?

Share this. Titles in this series. Refine Search. Content Type. Release Date. Showing results. It brings together researchers from …. Ed This book presents collaborative research presented by experts in the field of nonlinear science provides the reader with contemporary, cutting-edge, research works that bridge …. Ed This edited volume presents chapters on the dynamics of global climate change and global warming in the Middle East. In this region, it should be noted that even slightly warmer …. Ed In this book international expert authors provide solutions for modern fundamental problems including the complexity of computing of critical points for set-valued mappings, the ….

Book Nonlinear Systems, Vol. Ed This book presents an overview of the most recent advances in nonlinear science. Extending Gibbs's notion of …. National strategies often invoke the DIME diplomatic, information, military, and economic construct, as is the case when countries apply economic sanctions, or use diplomatic negotiations. Hillson explained that the DIME components constitute actions and consequential effects that can be highly interactive, complex, and unpredictable.

DSTA Dynamical Systems - Theory and Applications

As nations implement the DIME construct, the effects can be highly interrelated and can have unpredictable consequences. Technological advances in global information and communication infrastructures accelerate these complex interactions and the tempo of cause and effect. Complexity scientists are studying the causes and effects of seemingly unrelated events that have significant repercussions. Lagi, Bertrand, and Bar-Yam found that agricultural price increases in North America due to droughts were indirectly and inadvertently linked as a causal factor to violent protests in North Africa and the Middle East.

Technological advances in computers, Big Data, artificial intelligence, global information and communication networks have contributed to complex problem spaces. Big Data fosters the Age of Interactions through new technologies that enable rapid capture, processing, and storing of vast amounts of data, which result in heightened awareness, information overload, and unlimited access to information systems, individuals, and enterprises.

Exacerbating the problem domain are vast global networks of interconnected information nodes that create increases in complex interactions. Complexity is the state of having many different parts connected or related to each other in complicated, often non-linear interactions that are difficult to understand in a more complete manner.


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Highly complex problems are unpredictable and present dire consequences if not handled properly. They change over time, are unique from moment to moment, and often present shortened reaction times for involved decision-makers to address them Johnson, Complex problems, resulting from numerous non-linear interactions, can overwhelm traditional systems that cannot adapt quickly enough; cannot address multiple missions occurring simultaneously; and cannot process information quickly enough to make effective decision-making possible.

Calvano and John studied systems engineering methods aimed at handling complex problems. They found that traditional methods of engineering systems to meet well-defined static requirements are not sufficient to meet the adaptable and complex behavior required of engineered solutions for highly complex problem spaces. This research project studied complex adaptive systems of systems CASoS as a new class of systems with the potential to address highly complex problem spaces.

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This approach includes a system of systems that can produce intentionally designed and desired emergent behavior through the self-organization of their intelligent and purposeful constituent systems. By developing a theory for engineering a CASoS, this research contributes to the bodies of knowledge regarding systems, systems of systems SoS , and complex systems. The application of an approach based on CASoS theory to address certain complex problem spaces opens a new area of research within the domain of systems engineering.

In this paper, the authors describe the method of inquiry used to explore CASoS as solutions to highly complex problems, with a general discussion of classic grounded theory—an approach resulting in the emergence of theory based on creativity, reflection, conceptualization, and a self-critical iteration of ideas. The majority of the paper discusses the detailed application of classic grounded theory to produce the CASoS Engineering theory. A theory is systematically organized knowledge applicable in a relatively wide variety of circumstances, using a system of assumptions, accepted principles, and rules of procedure devised to analyze, predict, or otherwise explain the nature of behavior of a specified set of phenomena.

But it is also simply the best explanation which is available at the time. Remenyi, , p. Theory is a means of understanding and explaining observed phenomena. Adams, et. There are different research methods for developing theory. A common practice deduction follows the positivist scientific method of hypothesizing a theory and conducting experiments to test the theory, resulting in its adoption or rejection. The positivist approach is widely applied in the physical sciences.

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It relies on the scientific method, logic, and mathematics to develop theories that are predictive, reproducible, reliable, rigorous, and objective. Positivism assumes that the universe behaves according to inalterable, discoverable laws, and systems are merely the sum of their components Stol et al. Interpretivism, which is on the opposite side of the philosophical spectrum, is widely used in the social sciences and aims to understand and interpret human behavior.

Interpretivism relies largely on qualitative data and assumes that no universal truth or reality exists but rather reality is what people imagine it to be , and systems exhibit emergent behaviors not reducible to their component parts Stol et al. Another approach to developing theory is the classic grounded theory method, which is based on induction, and falls somewhere between positivism and interpretivism.

Induction is a method used to determine possible correlations of the deficiencies between the desired and calculated. These correlations are accepted into the design knowledge as new knowledge.

With the classic grounded theory method, a researcher studies observations and data in a structured and analytical way, thus enabling a theory that describes the phenomena to arise or emerge from the data. The results and findings are thus grounded in the empirical world. The classic grounded theory method builds , rather than tests, theory Patton, A recent review of software engineering research projects using grounded theory revealed a wide use of mixed methods based in positivism and interpretivism Stol et al.

However, this research project is neither positivist nor interpretivist. It does not develop a theory concerning observed physical phenomena or human behavior. Instead, its objective is to develop a theory for a new class of systems that shows potential as engineered solutions to highly complex problems. The research is rooted in pragmatism, and is largely theoretical or non-empirical, relying on examination of literature, reflection, and discourse with knowledgeable experts. This study focused on developing a critical theory that describes the class of CASoS solutions that can be applied to address highly complex problems.

For these reasons, the classic grounded theory approach was chosen to provide a rigorous methodology for performing this theoretical engineering research. Grounded theory is an effective methodology for pragmatic research based on rationalism a reason-based approach to understanding.

The classic grounded theory process relies on theory-method linkage, a rigorous yet iterative research methodology, and creative synthesis. Theory-method linkage is the important connection between data analysis and the formulation of theory.

Nano-Engineering of Complex Systems: Smart Nanocarriers for Biomedical Applications

The iterative process of data gathering, coding, and analyzing is illustrated in Figure 1. This figure shows how the classic grounded theory process begins with low-level substantive concepts and works toward high-level theoretical concepts using a series of analytic techniques.

Coding is the process of categorizing and organizing data about phenomena, identifying properties and causal conditions that influence phenomena, specifying strategies or actions that result from phenomena, and characterizing the context and influencing conditions. With theoretical sensitivity, a researcher can recognize and extract relevant information about the theory from the data.