Designing and Managing Complex Systems

Complexity science is a transdisciplinary subject involving complex systems that are multi-dimensional, consisting of a collection of interconnected relationships and parts. Managing complexity is an ongoing issue, particularly in organizations. Human Performance in Complex Systems introduces readers to the theory of complex systems, examining the role of the human within larger systems and the factors that affect human performance. The first section reviews the history of one particularly fruitful approach to complexity, providing an overview of complexity science. Next, the author discusses the current understanding of complex systems in a variety of domains including physical, biological, mechanical and organizational. Within these chapters author also, introduces the idea that there are similarities between the successful architecture and control of both biological and organizational systems. The third section focuses on case studies concerning failures and successes within complex systems, demonstrating how consilience design and control principles can lead to success and/or failure. The book concludes by using the preceding material to develop principles that can be applied for successful design and control of complex systems. Designing and Managing Complex Systems, (2023) 324pp. 978-0-323-91609-7 Front Cover 1 Designing and Managing Complex Systems 2 Designing and Managing Complex Systems 4 Copyright 5 Contents 8 Author biography 14 Preface 16 Acknowledgments 24 I - Cybernetics 26 1.1 - Control and communication 28 II - Learning from systems 42 2.1 - The simplification imperative 44 2.1.1 Model making 44 2.1.1.1 Chosen simplification 45 2.1.1.2 Forced simplification 47 2.1.1.3 Entrenched simplification 47 2.1.2 Necessary complexity and systems thinking 49 2.2 - The language of systems 54 2.2.1 Interlocking systems of reality 54 2.2.2 Structure and function of complex systems 55 2.2.3 Natural dynamics of complex systems 57 2.2.4 The need for complex systems 57 2.2.5 Tractability and effective complexity 59 2.2.5.1 The observer effect 62 2.2.6 Cynefin framework 63 2.2.6.1 Clear domain 65 2.2.6.2 Complicated domain 65 2.2.6.3 Complex domain 66 2.2.6.4 Chaotic domain 66 2.2.6.5 Disorder 67 2.2.6.6 The clear/chaotic boundary 67 2.2.7 How humans affect complexity 67 2.3 - Classes of systems 70 2.3.1 Classes of systems 70 2.3.2 Physical systems 71 2.3.2.1 Big Bang chemistry 71 2.3.2.2 Complexity and uncertainty at the subatomic level 73 2.3.3 Biological systems 76 2.3.3.1 Minimum gene sets 79 2.3.4 Entropy and constraint 80 2.3.4.1 Emergence 84 2.3.5 A consilient approach to the evolution of complex systems 87 2.3.6 Societal systems 88 2.3.7 Informational systems 90 2.3.8 Technical systems 90 2.3.8.1 Theoretic minima 92 2.3.9 Sociotechnical systems 93 2.4 - Neurobiological systems 94 2.4.1 Introduction 95 2.4.2 Structure and function of the nervous system 95 2.4.2.1 The central nervous systems 96 2.4.2.2 Neurons in the central nervous system 99 2.4.2.2.1 Types of neurons 100 2.4.2.3 Learning 101 2.4.2.4 The peripheral nervous system 103 2.4.3 Information processing 103 2.4.4 Functional scales in neurobiology 104 2.4.5 Decision-making 104 2.4.5.1 ACT-R 105 2.4.5.1.1 Workload 107 2.4.5.1.2 Summary of the anatomy of decision-making 108 2.4.5.2 The two modes of human decision-making 108 2.4.5.2.1 Mode 1 overview 109 2.4.5.2.2 Mode 2 overview 110 2.4.5.2.2.1 Summary of Mode 1 and Mode 2 111 2.4.5.2.2.2 How Mode 1 and Mode 2 interact and the role of workload 111 2.4.5.3 Heuristics and biases 112 2.4.5.4 Algorithmic decision-making 115 2.4.6 Levels of performance 116 2.4.6.1 Human failure modes 118 2.4.7 Neural dynamics and connectomics 121 2.4.8 Brain plasticity 124 2.4.9 The autonomic nervous system 125 2.4.9.1 Homeostasis 125 2.4.9.2 Allostasis 125 2.4.10 Feedback 127 2.4.11 The reticular activating system 128 2.4.12 Reflexes 129 2.5 - Sociotechnical systems 132 2.5.1 Introduction 132 2.5.2 Scales in sociotechnical systems 134 2.5.3 Taylorism, Fordism, and requisite metasystems 135 2.5.4 Dynamic safety model 137 2.5.4.1 Trade-offs 141 2.5.4.2 Procedural drift 143 2.5.4.3 Domain shift 144 2.5.5 The role of humans in sociotechnical systems 144 2.5.5.1 Joint cognitive systems 146 2.6 - Consilient dynamics across scales 148 2.6.1 Introduction 148 2.6.2 Summary of concepts covered 149 III - Creating and managing systems 156 3.1 - Introduction to part 3 158 3.1.1 Introduction 158 3.1.2 Definitions 158 3.1.3 Dynamics 159 3.1.4 Building up our understanding of systems 159 3.2 - Structure and function 162 3.2.1 Designing structure and assigning function 162 3.2.2 The Viable System Model 164 3.2.3 The Revised Viable System Model (rVSM) 166 3.2.3.1 SYS1 – implementation 168 3.2.3.2 SYS2 – communication 169 3.2.3.3 SYS3 – coordination 171 3.2.3.4 SYS4 – decision 173 3.2.3.4.1 SYS4GOAL 175 3.2.3.4.2 SYS4PERCEIVE 175 3.2.3.4.3 SYS4DECIDE 177 3.2.3.4.3.1 SYS4DECIDE failure modes 181 3.2.3.4.4 SYS4KNOWLEDGE 182 3.2.3.4.5 SYS4LEARN 183 3.2.3.5 SYS5 – identity 184 3.2.3.6 Alert signaling network 185 3.2.3.7 Transducers, amplifiers, and reducers 186 3.3 - Capability and adaptive capacity 188 3.3.1 Capability 188 3.3.2 Anticipating system dynamics 189 3.3.3 Adaptive capacity 190 3.4 - Engineering resilience 196 3.4.1 Introduction 196 3.4.2 A note about terminology 197 3.4.3 Resilience Engineering 198 3.4.4 Resilience and the operating point 199 3.4.4.1 System dynamics at the boundaries 200 3.4.5 Systemic failure modes and counterforces 202 3.4.6 Engineering resilience 204 3.4.6.1 The cornerstones of Resilience Engineering 204 3.4.6.2 The principles of Resilience Engineering 205 3.4.6.3 Resilient behaviors 207 3.4.7 Just culture 208 3.5 - Assessing the system properties of your organization 210 3.5.1 Introduction 210 3.5.2 Structure 211 3.5.2.1 SYS1 211 3.5.2.2 SYS2 211 3.5.2.3 SYS3 211 3.5.2.4 SYS4 212 3.5.2.5 SYS5 213 3.5.2.6 Transducers, complexity amplifiers, and complexity reducers 213 3.5.3 Function 213 3.5.4 Capability 213 3.5.5 Adaptive capacity 214 3.5.6 Resilience 214 3.5.7 Conclusion 215 IV - Case studies 218 4.1 - Challenger and Columbia1,2 220 4.1.1 Analysis 223 4.2 - Walmart, FEMA, and Hurricane Katrina1–4 226 4.2.1 Analysis 229 4.3 - Lake Peigneur1,2 232 4.3.1 Analysis 233 4.4 - The water temples of Bali1–3 236 4.4.1 Analysis 238 4.5 - The global financial crisis1–3 240 4.5.1 Analysis 243 4.6 - Continental Airlines1 248 4.6.1 Analysis 249 4.7 - Three Mile Island1–3 252 4.7.1 Analysis 254 4.8 - Cybersyn and the trucking strike1–3 256 4.8.1 Analysis 260 4.9 - Biological and informational viruses 264 4.9.1 Analysis 267 4.10 - Netflix1 272 4.10.1 Analysis 273 4.11 - Fukushima1–5 276 4.11.1 Analysis 278 4.12 - The Mumbai Dabbawalas1,2 280 4.12.1 Analysis 281 4.13 - Flash Crash1–4 284 4.13.1 Analysis 286 4.14 - Alphafold 21,2 288 4.14.1 Analysis 290 V - Conclusion 292 5.1 - Consilience with the arts 294 5.1.1 Introduction 294 5.1.2 Simple patterns and complex sounds 294 5.1.3 Complexity in the visual arts 297 5.2 - Conclusion 300 References 304 References 304 Index 316 A 316 B 316 C 316 D 317 E 317 F 318 G 318 H 318 I 318 J 319 K 319 L 319 M 319 N 319 O 320 P 320 Q 320 R 320 S 321 T 322 U 322 V 322 W 322 Back Cover 324 Back Cover 324 The systems that surround us are often multidimensional, and complex, consisting of a large collection of networked components with convoluted connections between them. Designing and managing such systems can be challenging, particularly in organizations. Designing and Managing Complex Systems introduces readers to the theory of complex systems, examining the role of human within larger systems, the factors that affect system performance, and how such systems can be optimized. The first section reviews the history of one particularly fruitful approach to complexity, one based on knowledge of the human nervous system. Next, the author discusses the current understanding of complex systems in a variety of domains including physical, biological, mechanical, and organizational. Within these chapters the author also introduces the idea that there are marked similarities in how complexity is successfully managed across these different domains and how the ideas from one domain can be useful in other domains. Next, these ideas are synthesized into a framework for successfully designing and managing complex systems. The fourth section focuses on case studies concerning failures and successes within complex systems. Provides an overview of the background and scope of complexity science Reviews current understanding of complex systems in a variety of domains (physical, biological, mechanical, and organizational) Introduces the idea of using successful techniques from one domain to help design and manage complex systems in other domains Includes case studies analysing failures and successes within complex systems Human Performance in Complex Systems introduces readers to the theory of complex systems, examining the role of humans within larger systems and the factors that affect human performance. Sections review the history of one particularly fruitful approach to complexity, providing an overview of complexity science that also discusses our current understanding of complex systems in a variety of domains, including physical, biological, mechanical and organizational. The author also introduces the idea that there are similarities between the successful architecture and control of both biological and organizational systems. Case studies concerning failures and successes within complex systems are also included. The book concludes by using the preceding material to develop principles that can be applied for successful design and control of complex systems.