Principles of Systems Science (Understanding Complex Systems)

This pioneering text provides a comprehensive introduction to systems structure, function, and modeling as applied in all fields of science and engineering. Systems understanding is increasingly recognized as a key to a more holistic education and greater problem solving skills, and is also reflected in the trend toward interdisciplinary approaches to research on complex phenomena. While the concepts and components of systems science will continue to be distributed throughout the various disciplines, undergraduate degree programs in systems science are also being developed, including at the authors’ own institutions. However, the subject is approached, systems science as a basis for understanding the components and drivers of phenomena at all scales should be viewed with the same importance as a traditional liberal arts education. Principles of Systems Science contains many graphs, illustrations, side bars, examples, and problems to enhance understanding. Frombasic principles of organization, complexity, abstract representations, and behavior (dynamics) to deeper aspects such as the relations between information, knowledge, computation, and system control, to higher order aspects such as auto-organization, emergence and evolution, the book provides an integrated perspective on the comprehensive nature of systems. It ends with practical aspects such as systems analysis, computer modeling, and systems engineering that demonstrate how the knowledge of systems can be used to solve problems in the real world. Each chapter is broken into parts beginning with qualitative descriptions that stand alone for students who have taken intermediate algebra. The second part presents quantitative descriptions that are based on pre-calculus and advanced algebra, providing a more formal treatment for students who have the necessary mathematical background. Numerous examples of systems from every realm of life, including the physical and biological sciences,humanities, social sciences, engineering, pre-med and pre-law, are based on the fundamental systems concepts of boundaries, components as subsystems, processes as flows of materials, energy, and messages, work accomplished, functions performed, hierarchical structures, and more. Understanding these basics enables further understanding both of how systems endure and how they may become increasingly complex and exhibit new properties or characteristics. Serves as a textbook for teaching systems fundamentals in any discipline or for use in an introductory course in systems science degree programs Addresses a wide range of audiences with different levels of mathematical sophistication Includes open-ended questions in special boxes intended to stimulate integrated thinking and class discussion Describes numerous examples of systems in science and society Captures the trend towards interdisciplinary research and problem solving About the Authors Preface Understanding Mental Models of the World: Cognitive Understanding Formal Models of the World: The Extension of Cognitive Understanding Why an Education in Systems Science? Why a Textbook on Systems Science? Why Is This Textbook the First of Its Kind? About the Math About a Central Theme: The Brain as a Complex Adaptive System About the Pedagogy About the Use of the Book For Students About the Think Boxes About the Quant Boxes About the Question Boxes For Teachers Bibliography Acknowledgements Contents Part I: Introduction to Systems Science 1.1 Getting Perspective and Orientation Chapter 1: A Helicopter View 1.1 Why Systems Science: The State of Knowledge and Understanding 1.2 The Distinctive Potential of Systems Science 1.2.1 What Is a Science? 1.2.2 What Is Systems Science? 1.3 Systems Science as a Mode of Inquiry 1.3.1 The Heritage of Atomism 1.3.2 Holism 1.3.3 System Causal Dynamics 1.3.4 Nonlinearity 1.4 The Principles of Systems Science 1.4.1 Principles as a Framework 1.4.2 Principle 1: Systemness 1.4.3 Principle 2: Systems Are Processes Organized in Structural and Functional Hierarchies 1.4.4 Principle 3: Systems Are Networks of Relations Among Components and Can Be Represented Abstractly as Such Networks of Relations 1.4.5 Principle 4: Systems Are Dynamic over Multiple Spatial and Time Scales 1.4.6 Principle 5: Systems Exhibit Various Kinds and Levels of Complexity 1.4.7 Principle 6: Systems Evolve 1.4.8 Principle 7: Systems Encode Knowledge and Receive and Send Information 1.4.9 Principle 8: Systems Have Regulatory Subsystems to Achieve Stability 1.4.10 Principle 9: Systems Can Contain Models of Other Systems 1.4.11 Principle 10: Sufficiently Complex, Adaptive Systems Can Contain Models of Themselves 1.4.12 Principle 11: Systems Can Be Understood (A Corollary of #9) 1.4.13 Principle 12: Systems Can Be Improved (A Corollary of #6) 1.5 The Exposition of Systems Science 1.6 An Outline History of Systems Science 1.6.1 Early Twentieth Century 1.6.2 Von Bertalanffy’s General Systems Theory 1.6.3 Cybernetics (See Chap. 9) 1.6.4 Information (See Chaps. 7 and 9) 1.6.5 Computation (See Chaps. 8 and 9) 1.6.6 Complex Systems (See Chap. 5) 1.6.7 Modeling Complex Systems (See Chap. 13) 1.6.8 Networks (See Chap. 4) 1.6.9 Self-Organization and Evolution (See Chaps. 10 and 11) 1.6.10 Autopoiesis (See Chaps. 10 and 11) 1.6.11 Systems Dynamics (See Chaps. 6 and 13) Bibliography and Further Reading Chapter 2: Systems Principles in the Real World: Understanding Drug-Resistant TB 2.1 Introduction 2.2 Drug-Resistant TB 2.2.1 Systemness: Bounded Networks of Relations Among Parts Constitute a Holistic Unit. Systems Interact with Other Systems. The Universe Is Composed of Systems of Systems 2.2.2 Systems Are Processes Organized in Structural and Functional Hierarchies 2.2.3 Systems Are Themselves and Can Be Represented Abstractly as Networks of Relations Between Components 2.2.4 Systems Are Dynamic on Multiple Time Scales 2.2.5 Systems Exhibit Various Kinds and Levels of Complexity 2.2.6 Systems Evolve 2.2.7 Systems Encode Knowledge and Receive and Send Information 2.2.8 Systems Have Regulation Subsystems to Achieve Stability 2.2.9 Systems Contain Models of Other Systems (e.g., Protocols for Interaction up to Anticipatory Models) 2.2.10 Sufficiently Complex, Adaptive Systems Can Contain Models of Themselves (e.g., Brains and Mental Models) 2.2.11 Systems Can Be Understood (A Corollary of #9): Science 2.2.12 Systems Can Be Improved (A Corollary of #6): Engineering 2.3 Conclusion Bibliography and Further Reading Part II: Structural and Functional Aspects 1.1 Properties of Systems Chapter 3: Organized Wholes 3.1 Introduction: Systems, Obvious and Not So Obvious 3.1.1 Systems from the Outside 3.1.2 Systems from the Inside 3.1.3 Systems Thinking 3.2 Philosophical Background 3.2.1 Ontological Status: Parts and Wholes 3.2.2 Epistemological Status: Knowledge and Information 3.2.2.1 Information 3.2.2.2 Knowledge 3.2.2.2.1 The Brain’s Natural Tendency 3.2.2.2.2 Subjectivity: Conception Driving Perception 3.2.2.2.3 Objectivity: Perception Based on Standards 3.3 Properties of Systems 3.3.1 Wholeness: Boundedness 3.3.1.1 Boundaries 3.3.1.1.1 Concrete Boundaries 3.3.1.1.2 Porous Boundaries 3.3.1.1.3 Fuzzy Boundaries 3.3.1.1.4 Conceptual Boundaries 3.3.1.1.5 Boundary Conditions 3.3.2 Composition 3.3.2.1 Components and Their “Personalities” 3.3.2.1.1 Modularity Versus Overlap 3.3.2.1.2 Boundary Conditions: External Personalities 3.3.2.1.3 Homogeneity Versus Heterogeneity 3.3.3 Internal Organization and Structure 3.3.3.1 Connectivity 3.3.3.1.1 Coupling Strength 3.3.3.1.2 Forces 3.3.3.1.3 Flows 3.3.3.2 Systems Within Systems 3.3.3.3 Hierarchical Organization 3.3.3.4 Complexity (A Preview) 3.3.3.4.1 Potential Complexity 3.3.3.4.2 Realized Complexity 3.3.3.5 Networks (Another Preview) 3.3.3.5.1 Function and Purpose 3.3.4 External Organization: System and Environment 3.3.4.1 Meaning of Environment 3.3.4.1.1 Environmental Flows and Messaging 3.3.4.1.2 Diffusion and Gradients 3.3.5 System Organization Summary 3.4 Conception of Systems 3.4.1 Conceptual Frameworks 3.4.1.1 Patterns 3.4.1.1.1 Spatial Patterns 3.4.1.1.2 Temporal Patterns 3.4.1.1.3 Maps 3.4.1.2 Properties and Their Measurement 3.4.1.3 Features 3.4.1.4 Classification 3.4.2 Pattern Recognition 3.4.2.1 Perception in the Human Brain 3.4.2.2 Machine Pattern Recognition 3.4.2.3 Learning or Encoding Pattern Mappings 3.5 Chapter Summary Bibliography and Further Reading Chapter 4: Networks: Connections Within and Without 4.1 Introduction: Everything Is Connected to Everything Else 4.2 The Fundamentals of Networks 4.2.1 Various Kinds of Networks 4.2.1.1 Physical Versus Logical 4.2.1.2 Fixed Versus Changing 4.2.1.3 Flow Networks 4.2.2 Attributes of Networks 4.2.2.1 Size and Composition 4.2.2.2 Density and Coupling Strength 4.2.2.3 Dynamics (Yet Another Preview) 4.2.3 Organizing Principles 4.2.3.1 Networks That Grow and/or Evolve 4.2.3.2 Small World Model 4.2.3.3 Hubs 4.2.3.4 Power Laws 4.2.3.5 Aggregation of Power 4.3 The Math of Networks 4.3.1 Graphs as Representations of Networks 4.3.2 Networks and the Structure of Systems 4.4 Networks and Complexity 4.5 Real-World Examples 4.5.1 Biological: A Cellular Network in the Body 4.5.2 The Earth Ecosystem as a Network of Flows 4.5.3 Food Webs in a Local Ecosystem 4.5.4 A Manufacturing Company as a Network Bibliography and Further Reading Chapter 5: Complexity 5.1 Introduction: A Concept in Flux 5.2 What Is Complexity? 5.2.1 Intuitions About Complexity 5.2.2 A Systems Definition of Complexity 5.2.2.1 Structural Hierarchy 5.2.2.1.1 Hierarchy Revisited 5.2.2.2 Real Hierarchies 5.2.2.2.2 Complex Hierarchy in the Tree of Life 5.2.2.2.3 Complex Hierarchy in Human Organizations 5.2.2.2.4 Complex Hierarchy in a Computing Machine 5.2.2.3 Functional Hierarchy 5.2.2.4 Complexity as Depth of a Hierarchical Tree 5.3 Other Perspectives on Complexity 5.3.1 Algorithm-Based Complexity 5.3.1.1 Time Complexity of Problems 5.3.1.2 Algorithmic Information Complexity 5.3.2 Complexity of Behavior 5.3.2.1 Cellular Automata 5.3.2.2 Fractals and Chaotic Systems 5.4 Additional Considerations on Complexity 5.4.1 Unorganized Versus Organized 5.4.2 Potential Versus Realized Complexity Parameters 5.5 Limits of Complexity 5.5.1 Component Failures 5.5.2 Process Resource or Sink Failures 5.5.3 Systemic Failures: Cascades 5.5.3.1 Aging 5.5.3.2 Collapse of Complex Societies 5.6 Summary of Complexity Bibliography and Further Reading Chapter 6: Behavior: System Dynamics 6.1 Introduction: Changes 6.2 Kinds of Dynamics 6.2.1 Motion and Interactions 6.2.2 Growth or Shrinkage 6.2.3 Development or Decline 6.2.4 Adaptivity 6.3 Perspectives on Behavior 6.3.1 Whole System Behavior: Black Box Analysis 6.3.2 Subsystem Behaviors: White Box Analysis 6.4 Systems as Dynamic Processes 6.4.1 Energy and Work 6.4.2 Thermodynamics 6.4.2.1 Energy Gradients 6.4.2.2 Entropy 6.4.2.3 Efficiency 6.4.3 Process Description 6.4.4 Black Box Analysis: Revisited 6.4.5 White Box Analysis Revisited 6.4.6 Process Transformations 6.4.6.1 Equilibrium 6.4.6.2 Systems in Transition 6.4.6.3 Systems in Steady State 6.4.6.4 Systems Response to Disturbances 6.4.6.4.1 Disturbances 6.4.6.4.2 Stability 6.4.6.4.3 Resilience 6.4.6.5 Messages, Information, and Change (One More Preview) 6.4.6.6 Process in Conceptual Systems 6.4.6.7 Predictable Unpredictability: Stochastic Processes 6.4.6.8 Chaos 6.5 An Energy System Example 6.5.1 An Initial Black Box Perspective 6.5.2 Opening Up the Box 6.5.3 How the System Works 6.5.4 So What? 6.6 Summary of Behavior Bibliography and Further Reading Part III: The Intangible Aspects of Organization: Maintaining and Adapting Chapter 7: Information, Meaning, Knowledge, and Communications 7.1 Introduction: What Is in a Word? 7.2 What Is Information? 7.2.1 Definitions 7.2.1.1 Communication 7.2.1.2 Message 7.2.1.3 Sender 7.2.1.4 Receiver 7.2.1.5 Observer 7.2.1.6 Channel 7.2.1.7 Signal 7.2.1.8 Noise 7.2.1.9 Codes 7.2.1.10 Protocols and Meaning 7.2.1.11 Data 7.3 Information Dynamics 7.3.1 Information and Entropy 7.3.2 Transduction, Amplification, and Information Processes 7.3.3 Surprise! 7.3.3.1 Modifying Expectations: An Introduction to Adaptation and Learning 7.3.3.2 Adaptation as a Modification in Expectancies 7.3.3.3 Internal Work in the Receiver 7.4 What Is Knowledge? 7.4.1 Context 7.4.2 Decision Processes 7.4.2.1 Decision Trees 7.4.2.2 Game Theory 7.4.2.3 Judgment 7.4.3 Anticipatory Systems 7.5 Summary of Information, Learning, and Knowledge: Along with a Surprising Result Bibliography and Further Reading Chapter 8: Computational Systems 8.1 Computational Process 8.1.1 A Definition of Computation 8.2 Types of Computing Processes 8.2.1 Digital Computation Based on Binary Elements 8.2.2 Electronic Digital Computers 8.2.3 Probabilistic Heuristic Computation 8.2.4 Adaptive, “Fuzzy” Heuristic Computation 8.2.5 Biological Brain Computation 8.2.5.1 Neural Computation 8.2.5.1.1 Synaptic Potentiation 8.2.5.1.2 Associative Potentiation with Temporal Ordering: Encoding Causal Relations 8.2.5.2 Neuronal Network Computation 8.2.5.2.1 Cortical Subsystems 8.2.5.2.1.1 Feature Detection in Sensory Cortex 8.2.5.2.1.2 Perception: Feature Integration in Association Cortex 8.2.5.2.1.3 Object Conception: Percept Integration in Association Cortex 8.2.5.2.1.4 Relation Conception: Object Integration and Causal Direction 8.2.5.2.1.5 Mental Models 8.2.5.3 Other Biological Computations 8.3 Purposes of Computation 8.3.1 Problem Solving 8.3.1.1 Mathematical Problems 8.3.1.2 Path Finding 8.3.1.3 Translation 8.3.1.4 Pattern Matching (Identification) 8.3.2 Data Capture and Storage 8.3.3 Modeling 8.4 Summary: The Ultimate Context of Computational Processes Bibliography and Further Reading Chapter 9: Cybernetics: The Role of Information and Computation in Systems 9.1 Introduction: Complex Adaptive Systems and Internal Control 9.2 Inter-system Communications 9.2.1 Communications and Cooperation 9.2.2 Informational Transactions 9.2.3 Markets as Protocols for Cooperation 9.3 Formal Coordination Through Hierarchical Control Systems: Cybernetics 9.3.1 Hierarchical Control Model Preview 9.4 Basic Theory of Control 9.4.1 Open Loop Control 9.4.2 Closed-Loop Control: The Control Problem 9.5 Factors in Control 9.5.1 Temporal Considerations 9.5.1.1 Sampling Rates and Time Scales 9.5.1.2 Sampling Frequency and Noise Issues 9.5.1.3 Computation Delay 9.5.1.4 Reaction Delay 9.5.1.5 Synchronization 9.5.2 Oscillations 9.5.3 Stability 9.6 Control Computations 9.6.1 PID Control 9.6.1.1 PID in Social Systems 9.6.1.2 Information Feed-Forward 9.6.1.3 Multiple Parameter Algorithms 9.6.2 Systemic Costs of Non-control Versus Costs of Control 9.6.3 More Advanced Control Methods 9.6.3.1 Adaptive Control: The “A” in CAS 9.6.3.2 Anticipatory Control 9.6.4 Summary of Operational Control 9.7 Coordination Among Processes 9.7.1 From Cooperation to Coordination 9.7.2 Coordination Between Processes: Logistical Control 9.7.2.1 A Basic Logistic Controller: Distribution of Resources via Budgets 9.7.2.2 Modeling Process Matching and Coordinated Dynamics 9.7.2.3 Regulating Buffers 9.7.2.4 Regulating Set Points 9.7.2.5 Coordinating Maintenance 9.7.2.6 Time Scales for Coordination 9.7.2.7 Process Control of the Coordination Process and the Coordination of Coordination! 9.7.3 Interface with the Environment: Tactical Control 9.7.3.1 Interface Processes 9.7.3.2 Active and Passive Interfaces 9.7.3.3 The Use of Feed-Forward Information 9.7.3.4 Coordination with External Entities 9.7.4 Summary of Coordination and Its Relation to Operations 9.8 Strategic Management 9.8.1 The Basic Strategic Problem 9.8.2 Basic Solutions 9.8.3 Environmental and Self-Models 9.8.4 Exploration Versus Exploitation 9.8.5 Plans (or Actually, Scenarios and Responses) 9.8.6 Summary of Coordination and Strategic Management 9.9 The Control Hierarchy 9.9.1 Hierarchical Management 9.9.1.1 Examples of Hierarchical Management in Nature and Human-­Built Organizations Living Cells The Brain Organizations Government 9.10 Problems in Hierarchical Management 9.10.1 Environmental Overload 9.10.1.1 Information Overload 9.10.1.2 Force Overload 9.10.1.3 Resource Loss 9.10.2 Internal Breakdown 9.10.2.1 Entropic Decay 9.10.2.2 Point Mutations 9.10.3 Imperfect Components 9.10.3.1 Stochastic Components 9.10.3.2 Heuristic Components 9.10.3.3 Internally Motivated Agents 9.10.4 Evolving Control Systems 9.11 Summary of Cybernetics Bibliography and Further Reading Part IV: Evolution Chapter 10: Auto-Organization and Emergence 10.1 Introduction: Toward Increasing Complexity 10.2 The Basic and General Features of Increasing Organization Over Time 10.2.1 Definitions 10.2.1.1 Order and Organization (or Order Versus Organization!) 10.2.1.2 Levels of Organization 10.2.1.3 Adaptation 10.2.1.4 Fit and Fitness 10.2.2 Evolution as a Kind of Algorithm 10.2.3 Increasing Complexity Through Time 10.2.4 No Free Lunch! 10.3 Auto-Organization 10.3.1 The Organizing Process 10.3.2 The Principles of Auto-Organizing Processes 10.3.2.1 Energy Partitioning 10.3.2.2 Energy Transfer 10.3.2.3 Cycles 10.3.2.4 Chance and Circumstances 10.3.2.5 Concentrations and Diffusion 10.3.2.6 Dissociation 10.3.2.7 Higher-Order Principles 10.3.2.7.1 Cooperation and Competition 10.3.2.7.2 Forced Moves 10.3.2.7.3 Path Dependency 10.3.3 Organizing, Reorganizing, and Stable Physical/Linkage Cycles 10.3.3.1 Order from Chaos 10.3.3.2 Selection of Minimum Energy Configurations 10.3.3.3 Hyper-Cycles and Autocatalysis 10.3.3.4 Self-Assembly 10.3.3.5 Auto-Organization and Selective Pressure 10.3.4 Auto-Organization Exemplified in Social Dynamics 10.4 Emergence 10.4.1 Emergent Properties 10.4.1.1 The Molecular Example 10.4.2 Emergent Functions 10.4.2.1 An Example from Society: Money 10.4.3 Cooperation and Competition as Emergent Organizing Principles 10.4.4 Emergent Complexity 10.4.5 The Emergence of Life 10.4.6 Supervenience and the Emergence of Culture 10.4.6.1 Language 10.4.6.2 Tool Making 10.5 Summary of Emergence Bibliography and Further Reading Chapter 11: Evolution 11.1 Beyond Adaptation 11.2 Evolution as a Universal Principle 11.2.1 The Environment Always Changes 11.2.2 Progress: As Increase in Complexity 11.2.3 The Mechanisms of Progressivity 11.2.4 Evolvability 11.2.5 Evolution as a Random Search Through Design Space 11.2.6 Biological and Supra-biological Evolution: The Paradigmatic Case 11.2.7 How Auto-Organization and Emergence Fit into the Models of Biological and Supra-biological Evolution 11.3 Replication 11.3.1 Knowledge Representations of Systems 11.3.2 Autonomous Replication 11.3.2.1 The Knowledge Medium in Biological and Supra-biological Systems 11.3.2.2 Copying Knowledge Structures: The Biological Example 11.3.2.3 Copying Knowledge Structures: The Supra-biological Example 11.4 Descent with Modification 11.4.1 Mutations: One Source of Variation 11.4.2 Mixing 11.4.3 Epigenetics 11.5 Selection 11.5.1 Competition 11.5.2 Cooperation 11.5.3 Coordination 11.5.4 Environmental Factors 11.6 Coevolution: The Evolution of Communities 11.6.1 The Coevolution of Ecosystems 11.6.2 The Coevolution of Culture 11.6.3 A Coevolutionary Model of Social-Cultural Process 11.6.3.1 Social Evolution 11.6.3.2 Society’s Fit with the Environment 11.7 Summary of Evolution Bibliography and Further Reading Part V: Methodological Aspects 1.1 Working with Systems Chapter 12: Systems Analysis 12.1 Introduction: Metascience Methodology 12.2 Gaining Understanding 12.2.1 Understanding Organization 12.2.2 Understanding Complexity 12.2.3 Understanding Behaviors (Especially Nonlinear) 12.2.4 Understanding Adaptability 12.2.5 Understanding Persistence 12.2.6 Understanding Forming and Evolving Systems 12.2.7 Cautions and Pitfalls 12.3 Decomposing a System 12.3.1 Language of System Decomposition 12.3.1.1 Lexical Elements 12.3.1.1.1 Sources and Sinks 12.3.1.1.2 Process/System 12.3.1.1.3 Flows 12.3.1.1.3.1 Organized Material Flows 12.3.1.1.3.2 Unorganized Material Flows 12.3.1.1.3.3 Energy Flows 12.3.1.1.3.4 Message Flow 12.3.1.1.4 Stocks or Buffers 12.3.1.1.5 Flow Controls 12.3.1.1.6 Interfaces 12.3.1.1.7 Values Slots 12.3.1.1.8 Sensors (Level or Flow) 12.3.1.1.9 Actuators 12.3.1.2 Uses in Decomposition 12.3.2 A Top-Down Process 12.3.2.1 Tools for Decomposition: Microscopes 12.3.2.2 Scale, Accuracy, and Precision of Measurements 12.3.3 Composition Hierarchy 12.3.4 Structural and Functional Decomposition 12.3.4.1 The System of Interest: Starting the Process 12.3.4.2 Decomposing Level 0 12.3.5 System Knowledge Base 12.3.6 The Structural Hierarchy (So Far) 12.3.7 Specifics Regarding Flows, Interfaces, and the Objects of Interest 12.3.8 Where We Are Now 12.3.9 Recursive Decomposition 12.3.9.1 When to Stop Decomposition 12.3.9.2 Tree Balance (or Not) 12.3.10 Open Issues, Challenges, and Practice 12.3.10.1 Recognizing Boundaries for Subsystems 12.3.10.2 Adaptable and Evolvable Systems 12.3.11 The Final Products of Decomposition 12.4 Life Cycle Analysis 12.5 Modeling a System 12.5.1 Modeling Engine 12.5.1.1 System Representation 12.5.1.2 Time Steps 12.5.1.3 Input Data 12.5.1.4 Instrumentation and Data Output Recording 12.5.1.5 Graphing the Results 12.5.2 The System Knowledge Base Is the Model! 12.5.3 Top-Down Model Runs and Decomposition 12.6 Examples 12.6.1 Cells and Organisms 12.6.2 Business Process 12.6.3 Biophysical Economics 12.6.4 Human Brain and Mind 12.7 Summary of Systems Analysis Bibliography and Further Reading Chapter 13: Systems Modeling 13.1 Introduction: Coming to a Better Understanding 13.1.1 Models Contained in Systems 13.1.2 What Is a Model? 13.1.3 Deeper Understanding 13.2 General Technical Issues 13.2.1 Resolution 13.2.2 Accuracy and Precision 13.2.3 Temporal Issues 13.2.4 Verification and Validation 13.2.5 Incremental Development 13.3 A Survey of Models 13.3.1 Kinds of Systems and Their Models 13.3.1.1 Physical 13.3.1.2 Mathematical 13.3.1.3 Statistical 13.3.1.4 Computerized (Iterated Solutions) 13.3.2 Uses of Models 13.3.2.1 Prediction of Behavior 13.3.2.2 Scenario Testing 13.3.2.3 Verification of Understanding 13.3.2.4 Design Testing 13.3.2.5 Embedded Control Systems 13.4 A Survey of Systems Modeling Approaches 13.4.1 System Dynamics 13.4.1.1 Background 13.4.1.2 Strengths of System Dynamics 13.4.1.3 Limitations of Stock and Flow 13.4.2 Agent-Based Modeling 13.4.2.1 Background 13.4.2.2 Modeling Framework 13.4.2.3 Definitions 13.4.2.3.1 Decision Processes 13.4.2.3.1.1 Decision Trees 13.4.2.3.1.2 Rule Based 13.4.2.3.1.3 Stochastic or Probabilistic 13.4.2.3.1.4 Judgment Based 13.4.2.3.2 Autonomy 13.4.2.3.3 Agents 13.4.2.4 Emergence of Macrostructures and Behaviors 13.4.2.5 Strengths of Agent-Based Modeling 13.4.2.6 Limitations of Agent-Based Modeling 13.4.3 Operations Research: An Overview 13.4.3.1 Strengths of OR 13.4.3.2 Weaknesses of OR 13.4.4 Evolutionary Models 13.4.4.1 Evolutionary Programming/Genetic Algorithms 13.4.4.2 Artificial Life 13.5 Examples 13.5.1 Modeling Population Dynamics with System Dynamics 13.5.1.1 The Model Diagram 13.5.1.2 Converting the Diagram to Computer Code 13.5.1.3 Getting the Output Graphed 13.5.1.4 Discussion 13.5.2 Modeling Social Insect Collective Intelligence 13.5.3 Biological Neurons: A Hybrid Agent-Based and System Dynamic Model 13.6 Summary of Modeling 13.6.1 Completing Our Understanding 13.6.2 Postscript: An Ideal Modeling Approach Bibliography and Further Reading Chapter 14: Systems Engineering 14.1 Introduction: Crafting Artifacts to Solve Problems 14.1.1 Problems to Be Solved 14.1.2 Affordance 14.1.3 Invention 14.1.4 Abstract Thinking 14.1.5 Crafting by Using Language, Art, and Mathematical Relations 14.1.5.1 Engineering and Science: Relations 14.1.5.2 Mathematics in Engineering 14.2 Problem Solving 14.2.1 Defining “Problem” 14.2.1.1 Definition 14.2.2 Modern Problems 14.2.3 Enter the Engineering of Systems 14.2.3.1 Role of the Systems Engineer 14.2.3.1.1 Domain Expert Engineers 14.2.3.1.2 The Systems Engineer 14.3 The System Life Cycle 14.3.1 Prenatal Development and Birth 14.3.2 Early Development 14.3.3 Useful Life: Maturing 14.3.4 Senescence and Obsolescence 14.3.5 Death (Decommissioning) 14.4 The Systems Engineering Process 14.4.1 Needs Assessment: The Client Role 14.4.2 Systems Analysis for Artifacts to be Developed 14.4.2.1 Problem Identification 14.4.2.2 Problem Analysis 14.4.2.3 Solution Analysis 14.4.2.3.1 What Is a Solution? 14.4.2.3.2 Feasibility 14.4.2.3.3 Sub-solutions 14.4.2.3.4 Modeling Sub-solutions 14.4.2.3.5 Specifications 14.4.2.4 Solution Design 14.4.2.5 Solution Construction 14.4.2.6 Solution Testing 14.4.2.7 Solution Delivery (Deployment) 14.4.2.8 Monitor Performance 14.4.2.9 Evaluate Performance 14.4.2.10 Performance Discrepancy Analysis 14.4.2.11 Upgrade/Modification Decision 14.4.3 Process Summary 14.5 Systems Engineering in the Real World Bibliography and Further Reading Index This Pioneering Text Provides A Comprehensive Introduction To Systems Structure, Function, And Modeling As Applied In All Fields Of Science And Engineering. Systems Understanding Is Increasingly Recognized As A Key To A More Holistic Education And Greater Problem Solving Skills, And Is Also Reflected In The Trend Toward Interdisciplinary Approaches To Research On Complex Phenomena. The Subject Of Systems Science, As A Basis For Understanding The Components And Drivers Of Phenomena At All Scales, Should Be Viewed With The Same Importance As A Traditional Liberal Arts Education. Principles Of Systems Science Contains Many Graphs, Illustrations, Side Bars, Examples, And Problems To Enhance Understanding.^ From Basic Principles Of Organization, Complexity, Abstract Representations, And Behavior (dynamics) To Deeper Aspects Such As The Relations Between Information, Knowledge, Computation, And System Control, To Higher Order Aspects Such As Auto-organization, Emergence And Evolution, The Book Provides An Integrated Perspective On The Comprehensive Nature Of Systems. It Ends With Practical Aspects Such As Systems Analysis, Computer Modeling, And Systems Engineering That Demonstrate How The Knowledge Of Systems Can Be Used To Solve Problems In The Real World. each Chapter Is Broken Into Parts Beginning With Qualitative Descriptions That Stand Alone For Students Who Have Taken Intermediate Algebra. The Second Part Presents Quantitative Descriptions That Are Based On Pre-calculus And Advanced Algebra, Providing A More Formal Treatment For Students Who Have The Necessary Mathematical Background.^ Understanding These Basics Enables Further Understanding Both Of How Systems Endure And How They May Become Increasingly Complex And Exhibit New Properties Or Characteristics. · Serves As A Textbook For Teaching Systems Fundamentals In Any Discipline Or For Use In An Introductory Course In Systems Science Degree Programs · Addresses A Wide Range Of Audiences With Different Levels Of Mathematical Sophistication · Includes Open-ended Questions In Special Boxes Intended To Stimulate Integrated Thinking And Class Discussion · Describes Numerous Examples Of Systems In Science And Society · Captures The Trend Towards Interdisciplinary Research And Problem Solving Part I: Introduction To Systems Science -- A Helicopter View -- Systems Principles In The Real World: Understanding Drug Resistant Tb -- Part Ii: Structural And Functional Aspects -- Organized Wholes -- Networks: Connections Within And Without -- Complexity -- Behavior: System Dynamics -- Part Iii: The Intangible Aspects Of Organization: Maintaining And Adapting -- Information, Meaning, Knowledge, And Communications -- Computational Systems -- Cybernetics: The Role Of Information And Computation In Systems -- Part Iv: Evolution -- Auto-organization And Emergence -- Evolution -- Part V: Methodological Aspects -- Systems Analysis -- Systems Modeling -- Systems Engineering. By George E. Mobus, Michael C. Kalton. This pioneering text provides a comprehensive introduction to systems structure, function, and modeling as applied in all fields of science and engineering. Systems understanding is increasingly recognized as a key to a more holistic education and greater problem solving skills, and is also reflected in the trend toward interdisciplinary approaches to research on complex phenomena. While the concepts and components of systems science will continue to be distributed throughout the various disciplines, undergraduate degree programs in systems science are also being developed, including at the authors own institutions. However, the subject is approached, systems science as a basis for understanding the components and drivers of phenomena at all scales should be viewed with the same importance as a traditional liberal arts education. Principles of Systems Science contains many graphs, illustrations, side bars, examples, and problems to enhance understanding. From basic principles of organization, complexity, abstract representations, and behavior (dynamics) to deeper aspects such as the relations between information, knowledge, computation, and system control, to higher order aspects such as auto-organization, emergence and evolution, the book provides an integrated perspective on the comprehensive nature of systems. It ends with practical aspects such as systems analysis, computer modeling, and systems engineering that demonstrate how the knowledge of systems can be used to solve problems in the real world. Each chapter is broken into parts beginning with qualitative descriptions that stand alone for students who have taken intermediate algebra. The second part presents quantitative descriptions that are based on pre-calculus and advanced algebra, providing a more formal treatment for students who have the necessary mathematical background. Numerous examples of systems from every realm of life, including the physical and biological sciences, humanities, social sciences, engineering, pre-med and pre-law, are based on the fundamental systems concepts of boundaries, components as subsystems, processes as flows of materials, energy, and messages, work accomplished, functions performed, hierarchical structures, and more. Understanding these basics enables further understanding both of how systems endure and how they may become increasingly complex and exhibit new properties or characteristics. This pioneering text provides a comprehensive introduction to systems structure, function, and modeling as applied in all fields of science and engineering. Systems understanding is increasingly recognized as a key to a more holistic education and greater problem solving skills, and is also reflected in the trend toward interdisciplinary approaches to research on complex phenomena. The subject of systems science, as a basis for understanding the components and drivers of phenomena at all scales, should be viewed with the same importance as a traditional liberal arts education. Principles of Systems Science contains many graphs, illustrations, side bars, examples, and problems to enhance understanding.^From basic principles of organization, complexity, abstract representations, and behavior (dynamics) to deeper aspects such as the relations between information, knowledge, computation, and system control, to higher order aspects such as auto-organization, emergence and evolution, the book provides an integrated perspective on the comprehensive nature of systems. It ends with practical aspects such as systems analysis, computer modeling, and systems engineering that demonstrate how the knowledge of systems can be used to solve problems in the real world. ℗lEach chapter is broken into parts beginning with qualitative descriptions that stand alone for students who have taken intermediate algebra. The second part℗l presents quantitative descriptions that are based on pre-calculus and advanced algebra, providing a more formal treatment for students who have the necessary mathematical background.^Understanding these basics enables further understanding both of how systems endure and how they may become increasingly complex and exhibit new properties or characteristics. ℗ʺ℗l℗l℗l℗l℗l℗l℗l℗l Serves as a textbook for teaching systems fundamentals in any discipline or for use in an introductory course in systems science degree programs ℗ʺ℗l℗l℗l℗l℗l℗l℗l℗l Addresses a wide range of audiences with different levels of mathematical sophistication ℗ʺ℗l℗l℗l℗l℗l℗l℗l℗l Includes open-ended questions in special boxes intended to stimulate integrated thinking and class discussion ℗ʺ℗l℗l℗l℗l℗l℗l℗l℗l Describes numerous examples of systems in science and society ℗ʺ℗l℗l℗l℗l℗l℗l℗l℗l Captures the trend towards interdisciplinary research and problem solving