Joseph J. Simpson

Entropy Metrics for System Identification and Analysis

Whole system metrics are valuable tools for use in systems science and engineering. Entropy metrics are defined, developed, and demonstrated in this paper. Based on classical systems engineering methods and practices, these entropy metrics indicate the degree of order/disorder in any given system. A physical-entropy based metric and an information-based entropy metric are aligned with the two primary components of a system: objects and relationships. The physical-entropy based metric is called a relational score, and the information-based metric is called an object score. A subsystem score, based on the relational score and object score, is also developed and presented in this paper. A well-defined process, using these metrics, is used to evaluate the reduction of entropy and complexity associated with any specific system. The metrics and processes developed in this work have a prose component, a graphic component, and a mathematical component. These three components are aligned with the systems science techniques developed by John N. Warfield.

Complexity reduction: A pragmatic approach

An increasing need for shared communication in disparate domains as well as the production of increasingly dynamic, large-scale systems has always been at the heart of the practice of systems engineering. As a branch of general systems theory, systems engineering was developed to address practical considerations posed by diverse organizations, environments, and cultures within which systems are designed, developed, and operated. Types and categories of complexity are used in this paper to focus the discussion on complexity and the reduction of complexity. Formal and theoretical foundations of systems science and systems engineering provided the basis upon which many effective systems engineering tools were built. This paper identifies some of the classical tools of systems science and systems engineering that manage complexity. Based on these classical tool components and principles, abstract relation types (ART) were developed to enhance the understanding and application of these tools. A pragmatic approach that is designed to reduce complexity as well as compare relative complexity reduction between and among methods is also presented. The direct value of systems engineering techniques as they are applied in any context is rooted in the ability of systems engineering techniques and systems engineering practitioners to reduce the cognitive complexity associated with the systems problem of interest.

Secure Software Education: A Contextual Model-Based Approach

This article establishes a context for secure information systems development as well as a set of models used to develop and apply a secure software production pedagogy. A generic system model is presented to support the system context development, and to provide a framework for discussing security relationships that exist between and among information systems and their applications. An asset protection model is tailored to provide a conceptual ontology for secure information system topics, and a stable logical framework that is independent of specific organizations, technologies, and their associated changes. This asset protection model provides a unique focus for each of the three primary professional communities associated with the development and operation of secure information systems. In this paper, a secure adaptive response model is discussed to provide an analytical tool to assess risk associated with the development and deployment of secure information systems, and to use as a security metric. A pedagogical model for information assurance curriculum development is then established in the context and terms of the developed secure information system models. The relevance of secure coding techniques to the production of secure systems, architectures, and organizational operations is also discussed.

System Evaluation and Description Using Abstract Relation Types (ART)

Two abstract relation types (ART) are developed to represent, describe and establish a computational framework for a system. An abstract relation type is closely related to and builds upon two fundamental ideas. The first idea is the binary relation and structural modeling techniques developed by John N. Warfield. The second idea is the concept of abstract data types. These two ideas are combined to create an abstract relation type that provides a structured representation and computational method for systems and system components. The complete system description approach is based on six abstract relation types: context, concept, functions, requirements, architecture, and test (CCFRAT). When combined with digraphs and other graphical representations of the matrix form, ART provides a powerful tool for the communication of complex system interactions to large system design teams.

Threshold Metric for Mapping Natural Language Relationships Among Objects

Formal system concepts, with direct connections to informal system representations, provide a “much-needed” pathway for the application of structure to a range of valuable systems methods, analyses, and techniques. The augmented model-exchange isomorphism (AMEI) provides a foundational link between formal and informal system representations. The AMEI focuses on the system structuring relationship that creates a system. This chapter expands that discussion to include the objects associated with a system and other contextual considerations.

Formal, theoretical aspects of systems engineering

In “Principles of Complex Systems for Systems Engineering,” S.A. Sheard and A. Mostashari [Syst Eng 12 (2009), 295–311] suggest issues related to a formal and theoretical background to systems engineering as well as the definition and analysis of complex systems (p. 295): “Systems engineering (SE) has evolved without much of a formal or theoretical background for its practices; instead it has relied on experientially developed principles and heuristics [Defoe, 1993; Rechtin, 1991; Stevens et al., 1998].” This communication outlines some of the existing formal, theoretical systems engineering literature, discusses the provided definition of complex systems, examines “Ordered Systems Engineering” in the context of existing systems engineering literature, and reflects on the principles of complex systems for systems engineering.