Scientific Foundations for Systems Engineering: Challenges and Strategies

Abstract

There is an increasing realization of the need for fundamental research in the science of systems engineering. The International Council on Systems Engineering vision document calls for theoretical foundations for systems architecting, systems design and systems understanding. During a recent NSF workshop, a number of knowledge areas ranging from mathematics, information sciences, physical sciences, systems science to human and social sciences were identified as possible sources from which the scientific foundation of systems engineering can be enhanced. However, the primary challenge facing the community lies in orchestrating the breadth and diversity of the many knowledge areas into a cohesive foundation. This paper briefly surveys systems science-related efforts across multiple application domains. The specific objectives in this paper are to present a classification of initiatives for developing foundations for systems engineering, and to discuss the challenges, and potential strategies forward, associated with systems science research. The classification is discussed using two case examples — the Internet and the air transportation system. Through these examples, some of the key research challenges and strategies are exemplified.

A Degree-Based Decision-Centric Model for Complex Networked Systems

“…In our previous study [5,6], we have proposed a framework towards laying complex systems engineering on decision-centric foundations. Such framework helps in characterizing the unique features of complex networked systems, as shown in Fig.…”

“…2(d) using probability density. The emergence of different types of degree distributions can be explained using the choice probability in Equation (5). First, when 1 = 0 , the choice probability is 1 indicating that all the nodes have the same linking probability, resulting in a random network with exponential degree distribution.…”

“…Towards developing such theoretical foundation, many initiatives have been taken. Davendralingam et al [5] categorize these initiatives into four classes: a) the application of theories to solve specific problems in one domain; b) abstraction of theories from application domains to domain-independent knowledge; c) the integration of theories from different domain; and d) the translation of a theory from one domain to another domain. In these four classes of initiatives, current systems engineering has been primary focused on the first two initiatives, i.e., the application and the abstraction.…”

“…In these four classes of initiatives, current systems engineering has been primary focused on the first two initiatives, i.e., the application and the abstraction. Though they are important, Davendralingam and coauthors [5] argue that the key to laying theoretical foundations for systems engineering depends on the other two classes of initiatives, i.e., the integration of theories and the translation of theories. These two classes are important because effective and cohesive integration helps determine the appropriate level of abstraction such that foundational research can be performed while ensuring the applicability of theories across domains.…”

Red‐Teaming as a Research Validation Method for Systems Engineering Thesis Students

“…To be justified in using something which is claimed to be "knowledge" to do anything the "knowledge" must be developed using a method which ensures it is true and provides justification that belief in that claimed knowledge is reasonable. Without prejudice concerning any other approach to this challenge, one approach which would satisfy this standard in research is to plan a methodology that can generate that about which a knowledge claim is made, and a second, independent, process which tests the veracity of the "knowledge" (Davendralingam et al, 2015). SE includes concern with the process by which the engineering work is performed and a different set of concerns with the development of the techniques that are used.…”