Modeling and Visualization of Complex Systems and Enterprises: Explorations of Physical, Human, Economic, and Social Phenomena (Stevens Institute Series on Complex Systems and Enterprises)

Explains multi-level models of enterprise systems and covers modeling methodology This book addresses the essential phenomena underlying the overall behaviors of complex systems and enterprises. Understanding these phenomena can enable improving these systems. These phenomena range from physical, behavioral, and organizational, to economic and social, all of which involve significant human components. Specific phenomena of interest and how they are represented depend on the questions of interest and the relevant domains or contexts. Modeling and Visualization of Complex Systems and Enterprises examines visualization of phenomena and how understanding the relationships among phenomena can provide the basis for understanding where deeper exploration is warranted. The author also reviews mathematical and computational models, defined very broadly across disciplines, which can enable deeper understanding. * Presents a 10 step methodology for addressing questions associated with the design or operation of complex systems and enterprises * Examines six archetypal enterprise problems including two from healthcare, two from urban systems, and one each from financial systems and defense systems * Provides an introduction to the nature of complex systems, historical perspectives on complexity and complex adaptive systems, and the evolution of systems practice Modeling and Visualization of Complex Systems and Enterprises is written for graduate students studying systems science and engineering and professionals involved in systems science and engineering, those involved in complex systems such as healthcare delivery, urban systems, sustainable energy, financial systems, and national security. William B. Rouse, PhD., is the Alexander Crombie Humphreys Chair of Economics in Engineering at Stevens Institute of Technology, and Director of the university wide Center for Complex Systems and Enterprises. He is also Professor Emeritus of Industrial and Systems Engineering at the Georgia Institute of Technology. He is a member of the National Academy of Engineering. Preface xi 1 Introduction and Overview 1 Systems Perspectives 2 Systems Movement 3 Philosophical Background 3 Seminal Concepts – Systems Science 5 Seminal Concepts – Economics/Cognition 6 Seminal Concepts – Operations Research 7 Seminal Concepts – Sociology 8 Complexity and Complex Systems 8 Complex Versus Complicated Systems 11 Systems Practice 13 Phenomena as the Starting Point 19 Oveview of Book 20 Chapter 1: Introduction and Overview 20 Chapter 2: Overall Methodology 21 Chapter 3: Perspectives on Phenomena 21 Chapter 4: Physical Phenomena 21 Chapter 5: Human Phenomena 21 Chapter 6: Economic Phenomena 22 Chapter 7: Social Phenomena 22 Chapter 8: Visualization of Phenomena 22 Chapter 9: Computational Methods and Tools 23 Chapter 10: Perspectives on Problem Solving 23 References 23 2 Overall Methodology 27 Introduction 27 Problem Archetypes 29 Deterring or Identifying Counterfeit Parts 29 Financial Systems and Bursting Bubbles 30 Human Responses and Urban Resilience 30 Traffic Control via Congestion Pricing 31 Impacts of Investments in Healthcare Delivery 31 Human Biology and Cancer 31 Comparison of Problems 32 Methodology 33 Summary 35 An Example 36 Supporting the Methodology 40 Conclusions 41 References 41 3 Perspectives on Phenomena 43 Introduction 43 Definitions 43 Historical Perspectives 46 Steam to Steamboats 46 Wind to Wings 47 Electricity to Electric Lights 47 Macro and Micro Physics 47 Probability and Utility 48 Contemporary Perspectives 48 Four Fundamental Forces 48 Computational Fluid Dynamics 49 Integrated Circuit Design 49 Supply Chain Management 50 Summary 50 Taxonomy of Phenomena 50 Behavioral and Social Systems 52 Problems versus Phenomena 54 Visualizing Phenomena 54 Conclusions 58 References 59 4 Physical Phenomena 61 Introduction 61 Natural Phenomena 61 Example – Human Biology 64 Example – Urban Oceanography 67 Designed Phenomena 69 Example – Vehicle Powertrain 73 Example – Manufacturing Processes 75 Deterring or Identifying Counterfeit Parts 76 Conclusions 80 References 80 5 Human Phenomena 83 Descriptive Versus Prescriptive Approaches 84 Models of Human Behavior and Performance 86 Example – Manual Control 87 Example – Problem Solving 89 Example – Multitask Decision Making 90 Traffic Control Via Congestion Pricing 92 Mental Models 95 Team Mental Models 99 Performing Arts Teams 101 Fundamental Limits 104 Conclusions 107 References 107 6 Economic Phenomena 111 Introduction 111 Microeconomics 113 Theory of the Firm 113 Theory of the Market 114 Example – Optimal Pricing 114 Example – Investing in People 118 Summary 119 Macroeconomics 119 Tax Rates Interest Rates and Inflation 120 Macroeconomic Models 126 Summary 128 Behavioral Economics 128 Prospect Theory 131 Risk Perception 132 Attribution Errors 133 Management Decision Making 134 Human Intuition 135 Intuition versus Analysis 136 Summary 137 Economics of Healthcare Delivery 137 Conclusions 139 References 140 7 Social Phenomena 143 Introduction 143 Emergent versus Designed Organizational Phenomena 143 Direct versus Representative Political Phenomena 144 Modeling Complex Social Systems 145 Example – Earth as a System 145 Physics-Based Formulations 149 Example – Castes and Outcastes 151 Network Theory 158 Game Theory 162 Example – Acquisition as a Game 165 Simulation 168 Example – Port and Airport Evacuation 170 Example – Emergence of Cities 171 Urban Resilience 172 A Framework for Urban Resilience 173 Summary 176 Conclusions 176 References 176 8 Visualization of Phenomena 179 Introduction 179 Human Vision as a Phenomenon 180 Basics of Visualization 180 Example – Space Shuttle Challenger 181 Purposes of Visualizations 183 Examples – Co-Citation Networks and Mobile Devices 184 Design Methodology 185 Use Case Illustrations 186 Example – Big Graphics and Little Screens 190 Visualization Tools 193 Data 195 Structure 195 Dynamics 195 Immersion Lab 196 Policy Flight Simulators 198 Background 198 Multilevel Modeling 199 Example – Employee Prevention and Wellness 200 People’s Use of Simulators 203 Conclusions 205 References 206 9 Computational Methods and Tools 209 Introduction 209 Modeling Paradigms 210 Dynamic Systems Theory 212 Control Theory 214 Estimation Theory 216 Queuing Theory 217 Network Theory 218 Decision Theory 221 Problem-Solving Theory 224 Finance Theory 225 Summary 228 Levels of Modeling 228 Representation to Computation 230 Dynamic Systems 230 Discrete-Event Systems 231 Agent-Based Systems 231 Optimization-Based Frame 231 Summary 233 Model Composition 233 Entangled States 233 Consistency of Assumptions 235 Observations 236 Computational Tools 236 Conclusions 237 References 238 10 Perspectives on Problem Solving 241 Introduction 241 What is? Versus What if? 242 Case Studies 243 Business Planning 243 New Product Planning 245 Technology Investments 248 Enterprise Transformation 250 Observations on Problem Solving 253 Starting Assumptions 253 Framing Problems 253 Implementing Solutions 255 Research Issues 255 Decomposition 256 Mapping 256 Scaling 257 Approximation 257 Identification 257 Parameterization 258 Propagation 258 Visualization 259 Curation 259 Conclusions 259 References 261 Index 263