Modeling and Managing Interdependent Complex Systems of Systems

Yacov Y. Haimes

• Publisher: Wiley-IEEE Press
• Year: 2018
• Print ISBN: 9781119173656
• eBook ISBN: 9781119173694
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Computer Engineering
• Index: Technology & Engineering

A comprehensive guide to the theory, methodology, and development for modeling systems of systems

Modeling and Managing Interdependent Complex Systems of Systems examines the complexity of, and the risk to, emergent interconnected and interdependent complex systems of systems in the natural and the constructed environment, and in its critical infrastructures. For systems modelers, this book focuses on what constitutes complexity and how to understand, model and manage it.Previous modeling methods for complex systems of systems were aimed at developing theory and methodologies for uncoupling the interdependencies and interconnections that characterize them. In this book, the author extends the above by utilizing public- and private- sector case studies; identifies, explores, and exploits the core of interdependencies; and seeks to understand their essence via the states of the system, and their dominant contributions to the complexity of systems of systems.

The book proposes a reevaluation of fundamental and practical systems engineering and risk analysis concepts on complex systems of systems developed over the past 40 years. This important resource:

• Updates and streamlines systems engineering theory, methodology, and practice as applied to complex systems of systems
• Introduces modeling methodology inspired by philosophical and conceptual thinking from the arts and sciences
• Models the complexity of emergent interdependent and interconnected complex systems of systems by analyzing their shared states, decisions, resources, and decisionmakers

Written for systems engineers, industrial engineers, managers, planners, academics and other professionals in engineering systems and the environment,this text is the resource for understanding the fundamental principles of modeling and managing complex systems of systems, and the risk thereto.

1
Modeling and Managing Interdependent Complex Systems of Systems: Fundamentals, Theory and Methodology

Part I: An Overview

1.I.1 Introduction

What does it mean to label systems as interdependent and interconnected complex systems of systems (Complex SoS)? Do we measure their complexity in terms of their subsystems’ multiple attributes and perspectives, their functionalities and resources, the number of shared states and decisions, resources, decision makers, and stakeholders, or in terms of their culture and organizational structure, etc.? Modeling is an amalgamation or symbiosis of the arts and the sciences. As the artist reconstructs images and ideas, scenes, people, and structures, so do the modelers of Complex SoS when they decompose and restructure the subsystems “from the inside out and from the outside in” and relate the components to each other through their natural, physical, organizational, and functional attributes, recreating the interdependent and interconnected entity. Using the building blocks of mathematical models (to be discussed in subsequent sections) and ultimately by exploiting the shared states and other essential entities among the subsystems, the modeler and other users are able to better understand Complex SoS. The term other common/shared essential entities includes shared decisions, decisionmakers, stakeholders, resources, organizational behavior and norms, policies and procedures, management, culture, and others. We adopt the premise that models are built to answer specific questions; they must be as simple as possible and as complex as required. Thus, modeling the natural environment and the constructed environment such as organizations, or a combination thereof, represents a similar challenge. Namely, how many perspectives of a single system must be considered by modelers to achieve close‐to‐a‐holistic model(s) in response to the required needs? And are we able to conceive of and discover all the essential attributes, characteristics, and perspectives of Complex SoS? Such open‐ended questions reinforce the notion that the modeling process is a journey of discovery, imagination, and creativity. When we think we have succeeded, we are likely to be proven wrong. This assertion ought to be interpreted constructively and philosophically, but never fatalistically. In other words, the modeling process is an open‐ended continuous journey of learning and exploration that is characterized by successes and failures through which progress is made and, eventually, models are declared representative and valuable.

What does it mean to characterize systems as Complex SoS? Indeed, the emergence of the complexity characterizing Complex SoS requires a reevaluation of their modeling, management, and communication. The evolution of the terms complexity and complex systems, their differing connotations during the last 50 years, and the ways in which they have led us to model and manage complexity are the subject of this book. Current models for Complex SoS are insufficient because too often they rely on the same modeling schema used for single systems. These models commonly fail to incorporate the complexity derived from the networks of interdependencies and interconnectedness (I‐I) characterizing Complex SoS.

In their essence, most cyber–physical, organizational, and governmental enterprises, now and in the future, belong to Complex SoS. Understanding their complexity and being able to characterize them can lead us to reevaluate our theory and methodologies as applied to single systems; more specifically being cognizant of and responsive to the emergent nature of Complex SoS, given the Evolving Base. The Evolving Base, discussed in Chapter 9, is represented by the following dynamic shifting rules and realities for each subsystem and for the entire Complex SoS: (i) goals and objectives; (ii) stakeholders, decision makers, and interest groups; (iii) organizational, political, and budgetary baselines; (iv) reorganization and reallocation of key personnel; (v) emergent technology and its deployment; and (vi) requirements, specifications, delivery, users, and clients (Haimes, 2012b).

In modeling Complex SoS, holism must be equally applied to natural and constructed environments, as well as to human and community activities and behaviors. The challenge is how to model the interface and the interplay among these activities that are not independent; rather, their I‐I are one manifestation of Complex SoS.

The above discussion is harmonious with the philosopher Jacob Bronowski’s (1978) seminal statements:

The world is totally connected. Whatever explanation we invent at any moment is a partial connection, and its richness derives from the richness of such connections as we are able to make. (p. 96)

There is no nerve without the muscle and no muscle without the nerve in the total animal. This is the same statement as the one I made about the total connection of the world…. (p. 99)

Of the human senses, Bronowski argues that arts mediated by the sense of light, like sculpture and painting, and arts that mediated by speech and sound, like the novel, drama, and music, dominate our outlook. Most of the time we use vision to give us information about the world and sound to give us information about other people in the world. How do we translate and build on Bronowski’s “vision” and “sound” in our modeling of Complex SoS? What kind of “instruments” do we need to model Complex SoS? In modeling, we commonly build on (i) domain knowledge, (ii) human and organizational behavior, (iii) the role of cyber–physical infrastructure in today’s quality of life of communities and individuals, (iv) systems engineering theory and methodology, (v) databases, and (vi) modeling experience, among others. What is the role of inference and perception in translating a system and its environment from reality into an abstract vision that is built on Bronowski’s and on other philosophers’ ideas in support of the fundamentals of state‐space theory (Bellman and Dreyfus, 1962, Nise, 2014)? The art and science of modeling is but an interpretation of the common multiple perspectives of Complex SoS used by modelers, namely, natural, physical, structural, organizational, or human behavior.

Fundamentally, this construable process represents a mental translation that implies a subjective cognitive understanding of each of the multiple perspectives of each system and their integration as a Complex SoS. Conceivably, two different modelers would interpret and perceive systems, subsystems, and, ultimately, the integrated Complex SoS, differently, given the amalgamation of the arts and sciences on which the modeling process is built. It is here where state‐space theory contributes to harmonizing the modeling process of Complex SoS. In particular, given the large number of states (variables) required to model and represent the multiple subsystems and their multitude of perspectives, as well as the necessity for brevity yet representativeness, modelers from different disciplines, and thus different perspectives, will naturally tend to be influenced by their unique personalities and backgrounds.

Furthermore, the large number of states that might be generated through the iterative, learn‐as‐you‐go modeling process necessitates the selection of a representative subset of shared states and other essential entities. Recall that we define essential entities to connote shared/common decisions, decision makers, stakeholders, resources, organizational setups, and history, among others. This selection of a minimum number of shared states and other essential entities with which to identify critical I‐I is the first step in identifying invaluable precursors to future impending failures. Note that the I‐I within Complex SoS constitute the essence of the sources of risk thereto. This step converts systems that heretofore were marginally connected in parallel to becoming connected in series. This process is pivotal for discovering one of the major sources of risk facing Complex SoS and the most important result of modeling the I‐I within and among systems and subsystems. Working together collaboratively, modelers can develop better models by augmenting the ingenuity of other modelers and scholars, as they collectively focus on and interpret the genesis of the I‐I characterizing the subsystems and, eventually, the entire Complex SoS. Alternatively, it is possible to envision separate modeling efforts by multiple modelers with a subsequent attempt to integrate the models to yield a better and more representative set of attributes of the overall Complex SoS. We ought to not overlook the modelers’ inherent ingenuity, background, talent, experiences, and innovativeness, contributing to the iterative modeling process that is characterized by a trial‐and‐error and a learn‐as‐you‐go process. In other words, the multipath exploration process that characterizes the modeling effort necessarily implies and even requires the intellectual creativity and energy of modelers of Complex SoS – a process that commonly yields to a better representation of the modeling efforts.

In his book Ageless Body Timeless Mind, the physician, philosopher, and author Deepak Chopra (1994) suggests the following three “models” of humans: physiology, mental capacity, and spirituality. No one would negate the notion that the human body is an interdependent and interconnected Complex SoS. Indeed, each organ is by itself a system of systems composed of multiple subsystems. The basic question is, can we model or represent a complete understanding of a person when we ignore one of the above three attributes identified by Chopra? The same principle of completeness/representativeness must apply to the natural and constructed Complex SoS. From...

Table of contents

• Cover
• Table of Contents
• Foreword
• 1 Modeling and Managing Interdependent Complex Systems of Systems: Fundamentals, Theory and Methodology
• 2 Modeling, Decomposition, and Multilevel Coordination of Complex Systems of Systems
• 3 Hierarchical Holographic Modeling and Multilevel Coordination of Complex Systems of Systems
• 4 Modeling Complex Systems of Systems with Phantom System Models
• 5 Complex Systems of Systems: Multiple Goals and Objectives
• 6 Hierarchical Coordinated Bayesian Modeling of Complex Systems of Systems
• 7 Hierarchical Multiobjective Modeling and Decision Making for Complex Systems of Systems
• 8 Modeling Economic Interdependencies among Complex Systems of Systems
• 9 Guiding Principles for Modeling and Managing Complex Systems of Systems
• 10 Modeling Cyber–Physical Complex Systems of Systems: Four Case Studies
• 11 Global Supply Chain as Complex Systems of Systems
• 12 Understanding and Managing the Organizational Dimension of Complex Systems of Systems
• 13 Software Engineering
• 14 Infrastructure Preparedness for Communities as Complex Systems of Systems
• 15 Modeling Safety of Transportation Complex Systems of Systems via Fault Trees
• Appendix
• Author Index
• Subject Index
• End User License Agreement