Dynamic Complexity Measures for Use in Complexity-Based System Design

How should organizations approach the evaluation of system complexity at the early stages of system design in order to inform decision making? Since system complexity can be understood and approached in several different ways, such evaluation is challenging. In this study, we define the term “system complexity factors” to refer to a range of different aspects of system complexity that may contribute differentially to systems engineering outcomes. Views on the absolute and relative importance of these factors for early–life cycle system evaluation are collected and analyzed using a qualitative questionnaire of International Council on Systems Engineers (INCOSE) members (n = 55). We identified and described the following trends in the data: there is little between‐participant agreement on the relative importance of system complexity factors, even for participants with a shared background and role; participants tend to be internally consistent in their ratings of the relative importance of system complexity factors. Given the lack of alignment on the relative importance of system complexity factors, we argue that successful evaluation of system complexity can be better ensured by explicit determination and discussion of the (possibly implicit) perspective(s) on system complexity that are being taken.

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Assaying the importance of system complexity for the systems engineering community

Potts

Sartor

Johnson

et al. 2020

“…Various publications examine what complexity means to a systems engineer. [19][20][21][22][23] Complex systems can be described in terms of dynamic complexity, that is, how a system evolves or changes over time and the difficulty associated with predicting behavior, 24,25 or socio-political complexity, which is the complexity arising from human cognitive limits or from multiple stakeholders exerting different effects on a system. 9,22 Alternatively, complex systems can be described in terms of their structural complexity, which arises from a combination of system scale, connectivity, and heterogeneity.…”

Section: Complex Systemmentioning

confidence: 99%

A network perspective on assessing system architectures: Foundations and challenges

Potts

Sartor

Johnson

et al. 2019

Organizations are increasingly faced with the challenge of architecting complex systems that must operate within a System of Systems context. While network science has offered usefully clear insights into product and system architectures, we seek to extend these approaches to evaluate enterprise system architectures. Here, we explore the application of graph‐theoretic methods to the analysis of two real‐world enterprise architectures (a military communications system and a search and rescue system) and to assess the relative importance of different architecture components. For both architectures, different topological measures of component significance identify differing network vertices as important. From this, we identify several significant challenges a system architect needs to be cognisant of when employing graph‐theoretic approaches to evaluate architectures; finding suitable abstractions of heterogeneous architectural elements and distinguishing between network‐structural properties and system‐functional properties. These challenges are summarized as five guiding principles for utilizing network science concepts for enterprise architecture evaluation.

“…The perspective of complexity used in this paper is with respect to the degree of difficulty in accurately predicting the future behavior. This complexity is determined by the system/SoS being observed, the capabilities of the observer, and the behavior that the observer is attempting to predict . Complexity associated with the architecture design of complex systems/SoS can be summarized in terms of (a) multiplicity of the number of design decisions, (b) diversity of the knowledge areas pertaining to the decision, and (c) inter‐relatedness due to the significant interdependencies and multiple implications of the decisions.…”

Section: Related Work and Challenges In Architecting Complex Systems mentioning

confidence: 99%

Decision learning framework for architecture design decisions of complex systems and system‐of‐systems

Raman

D’Souza

2019

Architecting complex systems and complex system‐of‐systems (SoS) have evinced keen interest recently. Architectural design decisions have a significant bearing on the operational measures of success, referred to as Measures of Effectiveness (MOEs), of the systems and SoS. Architecting complex systems and SoS involves making architecture design decisions despite uncertainty (due to knowledge gaps) on the implications associated with the decisions. The learning of whether the decision is optimal or not, and the impact on the MOEs and the emergent behavior of the SoS, often occur later, resulting in Learning Cycles. This paper proposes an integrated decision learning framework for architecture design decisions for complex systems and SoS. The proposed framework adopts a decision oriented view that factors the uncertainty associated with architectural decisions and the learning cycles and feedback loops experienced. The framework enables leverage of machine learning approaches to learn from the decision learning cycles experienced and factor it into the uncertainty assessments of the decisions. By inculcating various aspects such as knowledge gaps and learning cycles, by building models such as Learning Cycle Model and Uncertainty Model, and by incorporating deployment approaches such as codification of decision attributes and decision uncertainty assessments, the proposed framework enables progressive maturity of the architectural knowledge base and aids robustness in architecture design decisions.