Designing and Managing Complex Systems

Description

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. Front Cover Designing and Managing Complex Systems Designing and Managing Complex Systems Copyright Contents Author biography Preface Acknowledgments I - Cybernetics 1.1 - Control and communication II - Learning from systems 2.1 - The simplification imperative 2.1.1 Model making 2.1.1.1 Chosen simplification 2.1.1.2 Forced simplification 2.1.1.3 Entrenched simplification 2.1.2 Necessary complexity and systems thinking 2.2 - The language of systems 2.2.1 Interlocking systems of reality 2.2.2 Structure and function of complex systems 2.2.3 Natural dynamics of complex systems 2.2.4 The need for complex systems 2.2.5 Tractability and effective complexity 2.2.5.1 The observer effect 2.2.6 Cynefin framework 2.2.6.1 Clear domain 2.2.6.2 Complicated domain 2.2.6.3 Complex domain 2.2.6.4 Chaotic domain 2.2.6.5 Disorder 2.2.6.6 The clear/chaotic boundary 2.2.7 How humans affect complexity 2.3 - Classes of systems 2.3.1 Classes of systems 2.3.2 Physical systems 2.3.2.1 Big Bang chemistry 2.3.2.2 Complexity and uncertainty at the subatomic level 2.3.3 Biological systems 2.3.3.1 Minimum gene sets 2.3.4 Entropy and constraint 2.3.4.1 Emergence 2.3.5 A consilient approach to the evolution of complex systems 2.3.6 Societal systems 2.3.7 Informational systems 2.3.8 Technical systems 2.3.8.1 Theoretic minima 2.3.9 Sociotechnical systems 2.4 - Neurobiological systems 2.4.1 Introduction 2.4.2 Structure and function of the nervous system 2.4.2.1 The central nervous systems 2.4.2.2 Neurons in the central nervous system 2.4.2.2.1 Types of neurons 2.4.2.3 Learning 2.4.2.4 The peripheral nervous system 2.4.3 Information processing 2.4.4 Functional scales in neurobiology 2.4.5 Decision-making 2.4.5.1 ACT-R 2.4.5.1.1 Workload 2.4.5.1.2 Summary of the anatomy of decision-making 2.4.5.2 The two modes of human decision-making 2.4.5.2.1 Mode 1 overview 2.4.5.2.2 Mode 2 overview 2.4.5.2.2.1 Summary of Mode 1 and Mode 2 2.4.5.2.2.2 How Mode 1 and Mode 2 interact and the role of workload 2.4.5.3 Heuristics and biases 2.4.5.4 Algorithmic decision-making 2.4.6 Levels of performance 2.4.6.1 Human failure modes 2.4.7 Neural dynamics and connectomics 2.4.8 Brain plasticity 2.4.9 The autonomic nervous system 2.4.9.1 Homeostasis 2.4.9.2 Allostasis 2.4.10 Feedback 2.4.11 The reticular activating system 2.4.12 Reflexes 2.5 - Sociotechnical systems 2.5.1 Introduction 2.5.2 Scales in sociotechnical systems 2.5.3 Taylorism, Fordism, and requisite metasystems 2.5.4 Dynamic safety model 2.5.4.1 Trade-offs 2.5.4.2 Procedural drift 2.5.4.3 Domain shift 2.5.5 The role of humans in sociotechnical systems 2.5.5.1 Joint cognitive systems 2.6 - Consilient dynamics across scales 2.6.1 Introduction 2.6.2 Summary of concepts covered III - Creating and managing systems 3.1 - Introduction to part 3 3.1.1 Introduction 3.1.2 Definitions 3.1.3 Dynamics 3.1.4 Building up our understanding of systems 3.2 - Structure and function 3.2.1 Designing structure and assigning function 3.2.2 The Viable System Model 3.2.3 The Revised Viable System Model (rVSM) 3.2.3.1 SYS1 – implementation 3.2.3.2 SYS2 – communication 3.2.3.3 SYS3 – coordination 3.2.3.4 SYS4 – decision 3.2.3.4.1 SYS4GOAL 3.2.3.4.2 SYS4PERCEIVE 3.2.3.4.3 SYS4DECIDE 3.2.3.4.3.1 SYS4DECIDE failure modes 3.2.3.4.4 SYS4KNOWLEDGE 3.2.3.4.5 SYS4LEARN 3.2.3.5 SYS5 – identity 3.2.3.6 Alert signaling network 3.2.3.7 Transducers, amplifiers, and reducers 3.3 - Capability and adaptive capacity 3.3.1 Capability 3.3.2 Anticipating system dynamics 3.3.3 Adaptive capacity 3.4 - Engineering resilience 3.4.1 Introduction 3.4.2 A note about terminology 3.4.3 Resilience Engineering 3.4.4 Resilience and the operating point 3.4.4.1 System dynamics at the boundaries 3.4.5 Systemic failure modes and counterforces 3.4.6 Engineering resilience 3.4.6.1 The cornerstones of Resilience Engineering 3.4.6.2 The principles of Resilience Engineering 3.4.6.3 Resilient behaviors 3.4.7 Just culture 3.5 - Assessing the system properties of your organization 3.5.1 Introduction 3.5.2 Structure 3.5.2.1 SYS1 3.5.2.2 SYS2 3.5.2.3 SYS3 3.5.2.4 SYS4 3.5.2.5 SYS5 3.5.2.6 Transducers, complexity amplifiers, and complexity reducers 3.5.3 Function 3.5.4 Capability 3.5.5 Adaptive capacity 3.5.6 Resilience 3.5.7 Conclusion IV - Case studies 4.1 - Challenger and Columbia1,2 4.1.1 Analysis 4.2 - Walmart, FEMA, and Hurricane Katrina1–4 4.2.1 Analysis 4.3 - Lake Peigneur1,2 4.3.1 Analysis 4.4 - The water temples of Bali1–3 4.4.1 Analysis 4.5 - The global financial crisis1–3 4.5.1 Analysis 4.6 - Continental Airlines1 4.6.1 Analysis 4.7 - Three Mile Island1–3 4.7.1 Analysis 4.8 - Cybersyn and the trucking strike1–3 4.8.1 Analysis 4.9 - Biological and informational viruses 4.9.1 Analysis 4.10 - Netflix1 4.10.1 Analysis 4.11 - Fukushima1–5 4.11.1 Analysis 4.12 - The Mumbai Dabbawalas1,2 4.12.1 Analysis 4.13 - Flash Crash1–4 4.13.1 Analysis 4.14 - Alphafold 21,2 4.14.1 Analysis V - Conclusion 5.1 - Consilience with the arts 5.1.1 Introduction 5.1.2 Simple patterns and complex sounds 5.1.3 Complexity in the visual arts 5.2 - Conclusion References References Index A B C D E F G H I J K L M N O P Q R S T U V W Back Cover Back Cover