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El. knyga: Principles of Systems Science

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  • Formatas: PDF+DRM
  • Serija: Understanding Complex Systems
  • Išleidimo metai: 10-Nov-2014
  • Leidėjas: Springer-Verlag New York Inc.
  • Kalba: eng
  • ISBN-13: 9781493919208
Kitos knygos pagal šią temą:
Principles of Systems ScienceDidesnis paveikslėlis
  • Formatas: PDF+DRM
  • Serija: Understanding Complex Systems
  • Išleidimo metai: 10-Nov-2014
  • Leidėjas: Springer-Verlag New York Inc.
  • Kalba: eng
  • ISBN-13: 9781493919208
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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

Recenzijos

Principles of systems science consists of 14 chapters organized in five parts. This book is a feast--full of systems theory and sage guidance about systems practice. (Ernest Hughes, Computing Reviews, computingreviews.com, August, 2016)

Daugiau informacijos

"This textbook represents an amazing achievement in my view, both in terms of the breadth of the subject matter, which is vast, and the depth provided where necessary. Having reviewed the manuscript, I have already decided to offer a year-long graduate seminar at my university based on the text. I will also be adopting the text for other courses to augment or replace other sources. Some of the things I especially like about the text include its organization, its many illustrations, and its liberal use of "Question Boxes" that ask provocative questions of the reader. Although I've been a systems scientist for my entire career from the early 70's to now, every time I pick up the manuscript and start reading, I learn new things." -Wayne Wakeland, Ph.D., Professor and Systems Science Program Chair, School of the Environment, College of Liberal Arts and Sciences, Portland State University, Portland, Oregon, USA <"This book is interdisciplinary science at its best. While Systems Science is interdisciplinary at its core, it can be self-referencing in terms of methods and approach. Instead, Mobus and Kalton take a broad approach, exploring many of the most exciting scientific areas that must deal with complexity and causal interconnections, from system dynamics to network science. It is a walk through a series of fascinating discoveries in which the authors manage to be both exhaustive and clear. I found this book to be truly an experience!" -Ugo Bardi, Professor, University of Florence, Italy; Author, Extracted, 2014
Part I Introduction to Systems Science
1 A Helicopter View
3(40)
1.1 Why Systems Science: The State of Knowledge and Understanding
3(3)
1.2 The Distinctive Potential of Systems Science
6(4)
1.2.1 What Is a Science?
7(1)
1.2.2 What Is Systems Science?
8(2)
1.3 Systems Science as a Mode of Inquiry
10(7)
1.3.1 The Heritage of Atomism
10(1)
1.3.2 Holism
11(1)
1.3.3 System Causal Dynamics
12(2)
1.3.4 Nonlinearity
14(3)
1.4 The Principles of Systems Science
17(13)
1.4.1 Principles as a Framework
17(3)
1.4.2 Principle 1: Systemness
20(2)
1.4.3 Principle 2: Systems Are Processes Organized in Structural and Functional Hierarchies
22(1)
1.4.4 Principle 3: Systems Are Networks of Relations Among Components and Can Be Represented Abstractly as Such Networks of Relations
23(1)
1.4.5 Principle 4: Systems Are Dynamic over Multiple Spatial and Time Scales
24(1)
1.4.6 Principle 5: Systems Exhibit Various Kinds and Levels of Complexity
25(1)
1.4.7 Principle 6: Systems Evolve
26(1)
1.4.8 Principle 7: Systems Encode Knowledge and Receive and Send Information
26(1)
1.4.9 Principle 8: Systems Have Regulatory Subsystems to Achieve Stability
27(1)
1.4.10 Principle 9: Systems Can Contain Models of Other Systems
27(1)
1.4.11 Principle 10: Sufficiently Complex, Adaptive Systems Can Contain Models of Themselves
28(1)
1.4.12 Principle 11: Systems Can Be Understood (A Corollary of #9)
28(1)
1.4.13 Principle 12: Systems Can Be Improved (A Corollary of #6)
29(1)
1.5 The Exposition of Systems Science
30(2)
1.6 An Outline History of Systems Science
32(8)
1.6.1 Early Twentieth Century
33(1)
1.6.2 Von Bertalanffy's General Systems Theory
33(1)
1.6.3 Cybernetics (See Chap. 9)
34(1)
1.6.4 Information (See Chaps. 7 and 9)
35(1)
1.6.5 Computation (See Chaps. 8 and 9)
35(1)
1.6.6 Complex Systems (See Chap. 5)
36(2)
1.6.7 Modeling Complex Systems (See Chap. 13)
38(1)
1.6.8 Networks (See Chap. 4)
38(1)
1.6.9 Self-Organization and Evolution (See Chaps. 10 and 11)
39(1)
1.6.10 Autopoiesis (See Chaps. 10 and 11)
40(1)
1.6.11 Systems Dynamics (See Chaps. 6 and 13)
40(1)
Bibliography and Further Reading
40(3)
2 Systems Principles in the Real World: Understanding Drug-Resistant TB
43(30)
2.1 Introduction
43(1)
2.2 Drug-Resistant TB
44(24)
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
45(1)
2.2.2 Systems Are Processes Organized in Structural and Functional Hierarchies
46(2)
2.2.3 Systems Are Themselves and Can Be Represented Abstractly as Networks of Relations Between Components
48(1)
2.2.4 Systems Are Dynamic on Multiple Time Scales
49(2)
2.2.5 Systems Exhibit Various Kinds and Levels of Complexity
51(1)
2.2.6 Systems Evolve
52(2)
2.2.7 Systems Encode Knowledge and Receive and Send Information
54(2)
2.2.8 Systems Have Regulation Subsystems to Achieve Stability
56(2)
2.2.9 Systems Contain Models of Other Systems (e.g., Protocols for Interaction up to Anticipatory Models)
58(2)
2.2.10 Sufficiently Complex, Adaptive Systems Can Contain Models of Themselves (e.g., Brains and Mental Models)
60(2)
2.2.11 Systems Can Be Understood (A Corollary of #9): Science
62(3)
2.2.12 Systems Can Be Improved (A Corollary of #6): Engineering
65(3)
2.3 Conclusion
68(1)
Bibliography and Further Reading
69(4)
Part II Structural and Functional Aspects
3 Organized Wholes
73(64)
3.1 Introduction: Systems, Obvious and Not So Obvious
73(9)
3.1.1 Systems from the Outside
77(2)
3.1.2 Systems from the Inside
79(2)
3.1.3 Systems Thinking
81(1)
3.2 Philosophical Background
82(7)
3.2.1 Ontological Status: Parts and Wholes
82(2)
3.2.2 Epistemological Status: Knowledge and Information
84(5)
3.2.2.1 Information
84(1)
3.2.2.2 Knowledge
85(4)
3.3 Properties of Systems
89(30)
3.3.1 Wholeness: Boundedness
89(7)
3.3.1.1 Boundaries
90(6)
3.3.2 Composition
96(3)
3.3.2.1 Components and Their "Personalities"
97(2)
3.3.3 Internal Organization and Structure
99(17)
3.3.3.1 Connectivity
100(8)
3.3.3.2 Systems Within Systems
108(1)
3.3.3.3 Hierarchical Organization
108(1)
3.3.3.4 Complexity (A Preview)
108(5)
3.3.3.5 Networks (Another Preview)
113(3)
3.3.4 External Organization: System and Environment
116(3)
3.3.4.1 Meaning of Environment
116(3)
3.3.5 System Organization Summary
119(1)
3.4 Conception of Systems
119(15)
3.4.1 Conceptual Frameworks
122(7)
3.4.1.1 Patterns
122(3)
3.4.1.2 Properties and Their Measurement
125(2)
3.4.1.3 Features
127(1)
3.4.1.4 Classification
128(1)
3.4.2 Pattern Recognition
129(10)
3.4.2.1 Perception in the Human Brain
130(1)
3.4.2.2 Machine Pattern Recognition
131(1)
3.4.2.3 Learning or Encoding Pattern Mappings
132(2)
3.5
Chapter Summary
134(1)
Bibliography and Further Reading
134(3)
4 Networks: Connections Within and Without
137(32)
4.1 Introduction: Everything Is Connected to Everything Else
137(2)
4.2 The Fundamentals of Networks
139(15)
4.2.1 Various Kinds of Networks
140(4)
4.2.1.1 Physical Versus Logical
140(2)
4.2.1.2 Fixed Versus Changing
142(1)
4.2.1.3 Flow Networks
143(1)
4.2.2 Attributes of Networks
144(3)
4.2.2.1 Size and Composition
144(1)
4.2.2.2 Density and Coupling Strength
145(1)
4.2.2.3 Dynamics (Yet Another Preview)
145(2)
4.2.3 Organizing Principles
147(7)
4.2.3.1 Networks That Grow and/or Evolve
147(2)
4.2.3.2 Small World Model
149(1)
4.2.3.3 Hubs
150(2)
4.2.3.4 Power Laws
152(1)
4.2.3.5 Aggregation of Power
153(1)
4.3 The Math of Networks
154(3)
4.3.1 Graphs as Representations of Networks
154(2)
4.3.2 Networks and the Structure of Systems
156(1)
4.4 Networks and Complexity
157(1)
4.5 Real-World Examples
157(11)
4.5.1 Biological: A Cellular Network in the Body
158(1)
4.5.2 The Earth Ecosystem as a Network of Flows
159(2)
4.5.3 Food Webs in a Local Ecosystem
161(3)
4.5.4 A Manufacturing Company as a Network
164(4)
Bibliography and Further Reading
168(1)
5 Complexity
169(44)
5.1 Introduction: A Concept in Flux
169(1)
5.2 What Is Complexity?
170(27)
5.2.1 Intuitions About Complexity
172(1)
5.2.2 A Systems Definition of Complexity
173(24)
5.2.2.1 Structural Hierarchy
174(9)
5.2.2.2 Real Hierarchies
183(8)
5.2.2.3 Functional Hierarchy
191(2)
5.2.2.4 Complexity as Depth of a Hierarchical Tree
193(4)
5.3 Other Perspectives on Complexity
197(5)
5.3.1 Algorithm-Based Complexity
197(3)
5.3.1.1 Time Complexity of Problems
197(3)
5.3.1.2 Algorithmic Information Complexity
200(1)
5.3.2 Complexity of Behavior
200(2)
5.3.2.1 Cellular Automata
200(1)
5.3.2.2 Fractals and Chaotic Systems
201(1)
5.4 Additional Considerations on Complexity
202(2)
5.4.1 Unorganized Versus Organized
203(1)
5.4.2 Potential Versus Realized Complexity Parameters
203(1)
5.5 Limits of Complexity
204(8)
5.5.1 Component Failures
205(1)
5.5.2 Process Resource or Sink Failures
206(1)
5.5.3 Systemic Failures: Cascades
207(12)
5.5.3.1 Aging
207(1)
5.5.3.2 Collapse of Complex Societies
207(5)
5.6 Summary of Complexity
212(1)
Bibliography and Further Reading
212(1)
6 Behavior: System Dynamics
213(52)
6.1 Introduction: Changes
213(6)
6.2 Kinds of Dynamics
219(4)
6.2.1 Motion and Interactions
219(1)
6.2.2 Growth or Shrinkage
220(1)
6.2.3 Development or Decline
221(1)
6.2.4 Adaptivity
222(1)
6.3 Perspectives on Behavior
223(3)
6.3.1 Whole System Behavior: Black Box Analysis
224(1)
6.3.2 Subsystem Behaviors: White Box Analysis
225(1)
6.4 Systems as Dynamic Processes
226(30)
6.4.1 Energy and Work
226(1)
6.4.2 Thermodynamics
227(7)
6.4.2.1 Energy Gradients
228(1)
6.4.2.2 Entropy
228(1)
6.4.2.3 Efficiency
229(5)
6.4.3 Process Description
234(2)
6.4.4 Black Box Analysis: Revisited
236(1)
6.4.5 White Box Analysis Revisited
237(2)
6.4.6 Process Transformations
239(17)
6.4.6.1 Equilibrium
240(1)
6.4.6.2 Systems in Transition
240(1)
6.4.6.3 Systems in Steady State
241(1)
6.4.6.4 Systems Response to Disturbances
242(4)
6.4.6.5 Messages, Information, and Change (One More Preview)
246(2)
6.4.6.6 Process in Conceptual Systems
248(1)
6.4.6.7 Predictable Unpredictability: Stochastic Processes
249(2)
6.4.6.8 Chaos
251(5)
6.5 An Energy System Example
256(4)
6.5.1 An Initial Black Box Perspective
256(1)
6.5.2 Opening Up the Box
256(2)
6.5.3 How the System Works
258(1)
6.5.4 So What?
259(1)
6.6 Summary of Behavior
260(1)
Bibliography and Further Reading
261(4)
Part III The Intangible Aspects of Organization: Maintaining and Adapting
7 Information, Meaning, Knowledge, and Communications
265(46)
7.1 Introduction: What Is in a Word?
265(2)
7.2 What Is Information?
267(11)
7.2.1 Definitions
271(7)
7.2.1.1 Communication
271(1)
7.2.1.2 Message
271(1)
7.2.1.3 Sender
272(1)
7.2.1.4 Receiver
272(1)
7.2.1.5 Observer
272(1)
7.2.1.6 Channel
273(1)
7.2.1.7 Signal
274(1)
7.2.1.8 Noise
274(1)
7.2.1.9 Codes
274(2)
7.2.1.10 Protocols and Meaning
276(1)
7.2.1.11 Data
277(1)
7.3 Information Dynamics
278(19)
7.3.1 Information and Entropy
280(3)
7.3.2 Transduction, Amplification, and Information Processes
283(6)
7.3.3 Surprise!
289(8)
7.3.3.1 Modifying Expectations: An Introduction to Adaptation and Learning
290(1)
7.3.3.2 Adaptation as a Modification in Expectancies
291(4)
7.3.3.3 Internal Work in the Receiver
295(2)
7.4 What Is Knowledge?
297(10)
7.4.1 Context
299(2)
7.4.2 Decision Processes
301(2)
7.4.2.1 Decision Trees
301(1)
7.4.2.2 Game Theory
302(1)
7.4.2.3 Judgment
302(1)
7.4.3 Anticipatory Systems
303(4)
7.5 Summary of Information, Learning, and Knowledge: Along with a Surprising Result
307(2)
Bibliography and Further Reading
309(2)
8 Computational Systems
311(48)
8.1 Computational Process
311(5)
8.1.1 A Definition of Computation
313(3)
8.2 Types of Computing Processes
316(31)
8.2.1 Digital Computation Based on Binary Elements
316(2)
8.2.2 Electronic Digital Computers
318(10)
8.2.3 Probabilistic Heuristic Computation
328(3)
8.2.4 Adaptive, "Fuzzy" Heuristic Computation
331(2)
8.2.5 Biological Brain Computation
333(14)
8.2.5.1 Neural Computation
334(5)
8.2.5.2 Neuronal Network Computation
339(8)
8.2.5.3 Other Biological Computations
347(1)
8.3 Purposes of Computation
347(9)
8.3.1 Problem Solving
347(5)
8.3.1.1 Mathematical Problems
348(1)
8.3.1.2 Path Finding
349(1)
8.3.1.3 Translation
350(1)
8.3.1.4 Pattern Matching (Identification)
351(1)
8.3.2 Data Capture and Storage
352(1)
8.3.3 Modeling
353(3)
8.4 Summary: The Ultimate Context of Computational Processes
356(2)
Bibliography and Further Reading
358(1)
9 Cybernetics: The Role of Information and Computation in Systems
359(102)
9.1 Introduction: Complex Adaptive Systems and Internal Control
359(2)
9.2 Inter-system Communications
361(5)
9.2.1 Communications and Cooperation
361(2)
9.2.2 Informational Transactions
363(2)
9.2.3 Markets as Protocols for Cooperation
365(1)
9.3 Formal Coordination Through Hierarchical Control Systems: Cybernetics
366(3)
9.3.1 Hierarchical Control Model Preview
368(1)
9.4 Basic Theory of Control
369(5)
9.4.1 Open Loop Control
370(1)
9.4.2 Closed-Loop Control: The Control Problem
370(4)
9.5 Factors in Control
374(11)
9.5.1 Temporal Considerations
375(8)
9.5.1.1 Sampling Rates and Time Scales
375(3)
9.5.1.2 Sampling Frequency and Noise Issues
378(3)
9.5.1.3 Computation Delay
381(1)
9.5.1.4 Reaction Delay
382(1)
9.5.1.5 Synchronization
383(1)
9.5.2 Oscillations
383(1)
9.5.3 Stability
384(1)
9.6 Control Computations
385(19)
9.6.1 PID Control
385(7)
9.6.1.1 PID in Social Systems
389(1)
9.6.1.2 Information Feed-Forward
390(1)
9.6.1.3 Multiple Parameter Algorithms
391(1)
9.6.2 Systemic Costs of Non-control Versus Costs of Control
392(1)
9.6.3 More Advanced Control Methods
393(10)
9.6.3.1 Adaptive Control: The "A" in CAS
394(5)
9.6.3.2 Anticipatory Control
399(4)
9.6.4 Summary of Operational Control
403(1)
9.7 Coordination Among Processes
404(20)
9.7.1 From Cooperation to Coordination
406(1)
9.7.2 Coordination Between Processes: Logistical Control
407(12)
9.7.2.1 A Basic Logistic Controller: Distribution of Resources via Budgets
410(2)
9.7.2.2 Modeling Process Matching and Coordinated Dynamics
412(1)
9.7.2.3 Regulating Buffers
413(1)
9.7.2.4 Regulating Set Points
414(1)
9.7.2.5 Coordinating Maintenance
415(1)
9.7.2.6 Time Scales for Coordination
416(1)
9.7.2.7 Process Control of the Coordination Process and the Coordination of Coordination!
416(3)
9.7.3 Interface with the Environment: Tactical Control
419(4)
9.7.3.1 Interface Processes
419(1)
9.7.3.2 Active and Passive Interfaces
420(1)
9.7.3.3 The Use of Feed-Forward Information
421(1)
9.7.3.4 Coordination with External Entities
422(1)
9.7.4 Summary of Coordination and Its Relation to Operations
423(1)
9.8 Strategic Management
424(11)
9.8.1 The Basic Strategic Problem
425(4)
9.8.2 Basic Solutions
429(1)
9.8.3 Environmental and Self-Models
430(2)
9.8.4 Exploration Versus Exploitation
432(1)
9.8.5 Plans (or Actually, Scenarios and Responses)
433(1)
9.8.6 Summary of Coordination and Strategic Management
433(2)
9.9 The Control Hierarchy
435(5)
9.9.1 Hierarchical Management
437(3)
9.9.1.1 Examples of Hierarchical Management in Nature and Human-Built Organizations
437(3)
9.10 Problems in Hierarchical Management
440(13)
9.10.1 Environmental Overload
440(5)
9.10.1.1 Information Overload
441(2)
9.10.1.2 Force Overload
443(1)
9.10.1.3 Resource Loss
444(1)
9.10.2 Internal Breakdown
445(2)
9.10.2.1 Entropic Decay
445(1)
9.10.2.2 Point Mutations
446(1)
9.10.3 Imperfect Components
447(2)
9.10.3.1 Stochastic Components
447(1)
9.10.3.2 Heuristic Components
448(1)
9.10.3.3 Internally Motivated Agents
448(1)
9.10.4 Evolving Control Systems
449(4)
9.11 Summary of Cybernetics
453(1)
Bibliography and Further Reading
454(7)
Part IV Evolution
10 Auto-Organization and Emergence
461(66)
10.1 Introduction: Toward Increasing Complexity
461(2)
10.2 The Basic and General Features of Increasing Organization Over Time
463(15)
10.2.1 Definitions
464(9)
10.2.1.1 Order and Organization (or Order Versus Organization!)
464(1)
10.2.1.2 Levels of Organization
465(3)
10.2.1.3 Adaptation
468(1)
10.2.1.4 Fit and Fitness
469(4)
10.2.2 Evolution as a Kind of Algorithm
473(2)
10.2.3 Increasing Complexity Through Time
475(2)
10.2.4 No Free Lunch!
477(1)
10.3 Auto-Organization
478(26)
10.3.1 The Organizing Process
479(5)
10.3.2 The Principles of Auto-Organizing Processes
484(9)
10.3.2.1 Energy Partitioning
485(1)
10.3.2.2 Energy Transfer
486(1)
10.3.2.3 Cycles
486(1)
10.3.2.4 Chance and Circumstances
487(1)
10.3.2.5 Concentrations and Diffusion
488(1)
10.3.2.6 Dissociation
488(1)
10.3.2.7 Higher-Order Principles
489(4)
10.3.3 Organizing, Reorganizing, and Stable Physical/Linkage Cycles
493(9)
10.3.3.1 Order from Chaos
493(1)
10.3.3.2 Selection of Minimum Energy Configurations
494(3)
10.3.3.3 Hyper-Cycles and Autocatalysis
497(3)
10.3.3.4 Self-Assembly
500(1)
10.3.3.5 Auto-Organization and Selective Pressure
501(1)
10.3.4 Auto-Organization Exemplified in Social Dynamics
502(2)
10.4 Emergence
504(20)
10.4.1 Emergent Properties
505(2)
10.4.1.1 The Molecular Example
506(1)
10.4.2 Emergent Functions
507(1)
10.42.1 An Example from Society: Money
507(1)
10.4.3 Cooperation and Competition as Emergent Organizing Principles
508(3)
10.4.4 Emergent Complexity
511(1)
10.4.5 The Emergence of Life
512(4)
10.4.6 Supervenience and the Emergence of Culture
516(12)
10.4.6.1 Language
516(3)
10.4.6.2 Tool Making
519(5)
10.5 Summary of Emergence
524(1)
Bibliography and Further Reading
524(3)
11 Evolution
527(62)
11.1 Beyond Adaptation
527(1)
11.2 Evolution as a Universal Principle
528(13)
11.2.1 The Environment Always Changes
529(2)
11.2.2 Progress: As Increase in Complexity
531(2)
11.2.3 The Mechanisms of Progressivity
533(3)
11.2.4 Evolvability
536(1)
11.2.5 Evolution as a Random Search Through Design Space
537(2)
11.2.6 Biological and Supra-biological Evolution: The Paradigmatic Case
539(1)
11.2.7 How Auto-Organization and Emergence Fit into the Models of Biological and Supra-biological Evolution
539(2)
11.3 Replication
541(11)
11.3.1 Knowledge Representations of Systems
543(2)
11.3.2 Autonomous Replication
545(7)
11.3.2.1 The Knowledge Medium in Biological and Supra-biological Systems
546(3)
11.3.2.2 Copying Knowledge Structures: The Biological Example
549(2)
11.3.2.3 Copying Knowledge Structures: The Supra-biological Example
551(1)
11.4 Descent with Modification
552(5)
11.4.1 Mutations: One Source of Variation
554(1)
11.4.2 Mixing
555(1)
11.4.3 Epigenetics
556(1)
11.5 Selection
557(11)
11.5.1 Competition
560(1)
11.5.2 Cooperation
561(4)
11.5.3 Coordination
565(2)
11.5.4 Environmental Factors
567(1)
11.6 Coevolution: The Evolution of Communities
568(16)
11.6.1 The Coevolution of Ecosystems
569(1)
11.6.2 The Coevolution of Culture
570(2)
11.6.3 A Coevolutionary Model of Social-Cultural Process
572(18)
11.6.3.1 Social Evolution
575(3)
11.6.3.2 Society's Fit with the Environment
578(6)
11.7 Summary of Evolution
584(1)
Bibliography and Further Reading
585(4)
Part V Methodological Aspects
12 Systems Analysis
589(56)
12.1 Introduction: Metascience Methodology
589(1)
12.2 Gaining Understanding
590(5)
12.2.1 Understanding Organization
591(1)
12.2.2 Understanding Complexity
591(1)
12.2.3 Understanding Behaviors (Especially Nonlinear)
592(1)
12.2.4 Understanding Adaptability
592(1)
12.2.5 Understanding Persistence
592(1)
12.2.6 Understanding Forming and Evolving Systems
593(1)
12.2.7 Cautions and Pitfalls
593(2)
12.3 Decomposing a System
595(28)
12.3.1 Language of System Decomposition
596(7)
12.3.1.1 Lexical Elements
596(4)
12.3.1.2 Uses in Decomposition
600(3)
12.3.2 A Top-Down Process
603(1)
12.3.2.1 Tools for Decomposition: Microscopes
603(1)
12.3.2.2 Scale, Accuracy, and Precision of Measurements
604(1)
12.3.3 Composition Hierarchy
604(2)
12.3.4 Structural and Functional Decomposition
606(5)
12.3.4.1 The System of Interest: Starting the Process
607(1)
12.3.4.2 Decomposing Level 0
607(4)
12.3.5 System Knowledge Base
611(1)
12.3.6 The Structural Hierarchy (So Far)
611(1)
12.3.7 Specifics Regarding Flows, Interfaces, and the Objects of Interest
612(1)
12.3.8 Where We Are Now
613(1)
12.3.9 Recursive Decomposition
614(5)
12.3.9.1 When to Stop Decomposition
616(3)
12.3.9.2 Tree Balance (or Not)
619(1)
12.3.10 Open Issues, Challenges, and Practice
619(3)
12.3.10.1 Recognizing Boundaries for Subsystems
620(1)
12.3.10.2 Adaptable and Evolvable Systems
620(2)
12.3.11 The Final Products of Decomposition
622(1)
12.4 Life Cycle Analysis
623(1)
12.5 Modeling a System
624(6)
12.5.1 Modeling Engine
625(4)
12.5.1.1 System Representation
627(1)
12.5.1.2 Time Steps
627(1)
12.5.1.3 Input Data
628(1)
12.5.1.4 Instrumentation and Data Output Recording
628(1)
12.5.1.5 Graphing the Results
628(1)
12.5.2 The System Knowledge Base Is the Model!
629(1)
12.5.3 Top-Down Model Runs and Decomposition
629(1)
12.6 Examples
630(13)
12.6.1 Cells and Organisms
630(2)
12.6.2 Business Process
632(3)
12.6.3 Biophysical Economics
635(2)
12.6.4 Human Brain and Mind
637(6)
12.7 Summary of Systems Analysis
643(1)
Bibliography and Further Reading
644(1)
13 Systems Modeling
645(54)
13.1 Introduction: Coming to a Better Understanding
645(6)
13.1.1 Models Contained in Systems
647(1)
13.1.2 What Is a Model?
648(2)
13.1.3 Deeper Understanding
650(1)
13.2 General Technical Issues
651(3)
13.2.1 Resolution
651(1)
13.2.2 Accuracy and Precision
652(1)
13.2.3 Temporal Issues
653(1)
13.2.4 Verification and Validation
653(1)
13.2.5 Incremental Development
654(1)
13.3 A Survey of Models
654(7)
13.3.1 Kinds of Systems and Their Models
655(3)
13.3.1.1 Physical
655(1)
13.3.1.2 Mathematical
655(1)
13.3.1.3 Statistical
656(1)
13.3.1.4 Computerized (Iterated Solutions)
657(1)
13.3.2 Uses of Models
658(3)
13.3.2.1 Prediction of Behavior
658(1)
13.3.2.2 Scenario Testing
659(1)
13.3.2.3 Verification of Understanding
659(1)
13.3.2.4 Design Testing
660(1)
13.3.2.5 Embedded Control Systems
660(1)
13.4 A Survey of Systems Modeling Approaches
661(21)
13.4.1 System Dynamics
661(5)
13.4.1.1 Background
661(3)
13.4.1.2 Strengths of System Dynamics
664(1)
13.4.1.3 Limitations of Stock and Flow
664(2)
13.4.2 Agent-Based Modeling
666(11)
13.4.2.1 Background
666(1)
13.4.2.2 Modeling Framework
666(2)
13.4.2.3 Definitions
668(8)
13.4.2.4 Emergence of Macrostructures and Behaviors
676(1)
13.4.2.5 Strengths of Agent-Based Modeling
676(1)
13.4.2.6 Limitations of Agent-Based Modeling
677(1)
13.4.3 Operations Research: An Overview
677(4)
13.4.3.1 Strengths of OR
680(1)
13.4.3.2 Weaknesses of OR
680(1)
13.4.4 Evolutionary Models
681(1)
13.4.4.1 Evolutionary Programming/Genetic Algorithms
681(1)
13.4.4.2 Artificial Life
682(1)
13.5 Examples
682(13)
13.5.1 Modeling Population Dynamics with System Dynamics
682(4)
13.5.1.1 The Model Diagram
683(1)
13.5.1.2 Converting the Diagram to Computer Code
683(1)
13.5.1.3 Getting the Output Graphed
684(1)
13.5.1.4 Discussion
685(1)
13.5.2 Modeling Social Insect Collective Intelligence
686(1)
13.5.3 Biological Neurons: A Hybrid Agent-Based and System Dynamic Model
687(8)
13.6 Summary of Modeling
695(3)
13.6.1 Completing Our Understanding
695(1)
13.6.2 Postscript: An Ideal Modeling Approach
696(2)
Bibliography and Further Reading
698(1)
14 Systems Engineering
699(34)
14.1 Introduction: Crafting Artifacts to Solve Problems
699(5)
14.1.1 Problems to Be Solved
700(1)
14.1.2 Affordance
701(1)
14.1.3 Invention
701(1)
14.1.4 Abstract Thinking
702(1)
14.1.5 Crafting by Using Language, Art, and Mathematical Relations
702(2)
14.1.5.1 Engineering and Science: Relations
703(1)
14.1.5.2 Mathematics in Engineering
704(1)
14.2 Problem Solving
704(5)
14.2.1 Defining "Problem"
705(1)
14.2.1.1 Definition
705(1)
14.2.2 Modem Problems
706(1)
14.2.3 Enter the Engineering of Systems
707(2)
14.2.3.1 Role of the Systems Engineer
708(1)
14.3 The System Life Cycle
709(5)
14.3.1 Prenatal Development and Birth
710(1)
14.3.2 Early Development
711(1)
14.3.3 Useful Life: Maturing
711(1)
14.3.4 Senescence and Obsolescence
712(1)
14.3.5 Death (Decommissioning)
713(1)
14.4 The Systems Engineering Process
714(16)
14.4.1 Needs Assessment: The Client Role
716(2)
14.4.2 Systems Analysis for Artifacts to be Developed
718(11)
14.4.2.1 Problem Identification
718(2)
14.4.2.2 Problem Analysis
720(1)
14.4.2.3 Solution Analysis
721(3)
14.4.2.4 Solution Design
724(1)
14.4.2.5 Solution Construction
724(2)
14.4.2.6 Solution Testing
726(1)
14.4.2.7 Solution Delivery (Deployment)
726(1)
14.4.2.8 Monitor Performance
726(1)
14.4.2.9 Evaluate Performance
727(1)
14.4.2.10 Performance Discrepancy Analysis
727(1)
14.4.2.11 Upgrade/Modification Decision
728(1)
14.4.3 Process Summary
729(1)
14.5 Systems Engineering in the Real World
730(1)
Bibliography and Further Reading
731(2)
Index 733
George E. Mobus is an Associate Professor of Computer Science & Systems and Computer Engineering & Systems in the Institute of Technology at the University of Washington Tacoma. In addition to teaching computer science and engineering courses, he teaches courses in systems science to a broad array of students from across the campus. He received his PhD in computer science from the University of North Texas in 1994. His dissertation, and subsequent research program at Western Washington University, involved developing autonomous robot agents by emulating natural intelligence as opposed to using some form of artificial intelligence. He is reviving this research agenda now that hardware elements have caught up with the processing requirements for simulating real biological neurons. He also received an MBA from San Diego State University in 1983, doing a thesis on the modeling of decision support systems based on the hierarchical cybernetic principles presented in this volume. Hedid this while actually managing an embedded systems manufacturing and engineering company in Southern California. His baccalaureate degree was earned at the University of Washington (Seattle) in 1973, in zoology. He studied the energetics of living systems and the interplay between information, evolution, and complexity. By using some control algorithms that he had developed in both his undergraduate and MBA degrees in programming embedded control systems he solved some interesting problems that led to promotion from a software engineer (without a degree) to the top spot in the company.





Michael C. Kalton is Professor Emeritus of Interdisciplinary Arts and Sciences at the University of Washington Tacoma. He came to systems science through the study of how cultures arise from and reinforce different ways of thinking about and interacting with the world. After receiving a Bachelors degree in Philosophy and Letters, a Masters degree in Greek, and a Licentiate in Philosophy from St. Louis University, he went to Harvard University where in 1977 he received a joint Ph.D. degree in East Asian Languages and Civilizations, and Comparative Religion.  He has done extensive research and publication on the Neo-Confucian tradition, the dominant intellectual and spiritual tradition throughout East Asia prior to the 20th century. Environmental themes of self-organizing relational interdependence and the need to fit in the patterned systemic flow of life drew his attention due to their resonance with East Asian assumptions about the world.   Ecosystems joined social systems in his research and teaching, sharing a common matrix in the study of complex systems, emergence and evolution.  The interdisciplinary character of his program allowed this integral expansion of his work; systems thinking became the thread of continuity in courses ranging from the worlds great social, religious, and intellectual traditions to environmental ethics and the systems dynamics of contemporary society.  He sees a deep and creative synergy between pre-modern Neo-Confucian thought and contemporary systems science; investigating this potential cross-fertilization is now his major research focus.
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