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E-grāmata: Modeling Human System Interaction - Philosophical and Methodological Considerations, with Examples: Philosophical and Methodological Considerations, with Examples [Wiley Online]

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This book provides an overview of the rational and pragmatic reasons for modeling in the human-technology system, including the various pitfalls and difficulties, and also discusses models and tradeoffs for large-scale societal systems. Following the introduction, the first four chapters of the book are devoted to examining the considerations in rational modeling in any field of science or engineering. A distinction is made between denotation (as required in science) and connotation (as common in the arts) in representing something in a useful way. Chapter 5 is a transition chapter that describes various forms of representation that a model can take, with examples. Chapters 6-9 provide examples of specific models that fit each of the four stages of human-system interaction mentioned above. In these four chapters, the gist of each model is first described with words only, and then with brief relevant mathematics for those interested. In both cases the intent is to provide at least the most important elements of the model, with references cited for further reading about that particular model. Chapter 10 discusses several categories of major societal issues, particularly with respect to analyzing trade-off relationships.
Preface xi
Introduction 1(4)
1 Knowledge
5(12)
Gaining New Knowledge
5(2)
Scientific Method: What Is It?
7(1)
Further Observations on the Scientific Method
8(2)
Reasoning Logically
10(1)
Public (Objective) and Private (Subjective) Knowledge
11(1)
The Role of Doubt in Doing Science
11(1)
Evidence: Its use and Avoidance
12(1)
Metaphysics and its Relation to Science
12(1)
Objectivity, Advocacy, and Bias
13(1)
Analogy and Metaphor
14(3)
2 What is a Model?
17(16)
Defining "Model"
17(3)
Model Attributes: A New Taxonomy
20(5)
Examples of Models in Terms of the Attributes
25(2)
Why Make the Effort to Model?
27(1)
Attribute Considerations in Making Models Useful
27(3)
Social Choice
30(1)
What Models are Not
31(2)
3 Important Distinctions in Modeling
33(8)
Objective and Subjective Models
33(2)
Simple and Complex Models
35(1)
Descriptive and Prescriptive (Normative) Models
36(1)
Static and Dynamic Models
36(1)
Deterministic and Probabilistic Models
36(1)
Hierarchy of Abstraction
37(1)
Some Philosophical Perspectives
38(3)
4 Forms of Representation
41(10)
Verbal Models
41(1)
Graphs
42(2)
Maps
44(1)
Schematic Diagrams
45(1)
Logic Diagrams
46(2)
Crisp Versus Fuzzy Logic (see also Appendix, Section "Mathematics of Fuzzy Logic")
48(2)
Symbolic Statements and Statistical Inference (see also Appendix, Section "Mathematics of Statistical Inference From Evidence")
50(1)
5 Acquiring Information
51(18)
Information Communication (see also Appendix, Section "Mathematics of Information Communication")
51(2)
Information Value (see also Appendix, Section "Mathematics of Information Value")
53(1)
Logarithmic-Like Psychophysical Scales
54(1)
Perception Process (see also Appendix, Section "Mathematics of the Brunswik/Kirlik Perception Model")
54(1)
Attention
55(1)
Visual Sampling (see also Appendix, Section "Mathematics of How Often to Sample")
56(2)
Signal Detection (see also Appendix, Section "Mathematics of Signal Detection")
58(1)
Situation Awareness
59(1)
Mental Workload (see also Appendix, Section "Research Questions Concerning Mental Workload")
60(4)
Experiencing What is Virtual: New Demands for Human-System Modeling (see also Appendix, Section "Behavior Research Issues in Virtual Reality")
64(5)
6 Analyzing the Information
69(8)
Task Analysis
69(1)
Judgment Calibration
70(2)
Valuation/Utility (see also Appendix, Section "Mathematics of Human Judgment of Utility")
72(1)
Risk and Resilience
73(1)
Definition of Risk
73(1)
Meaning of Resilience
73(2)
Trust
75(2)
7 Deciding on Action
77(6)
What is Achievable
77(1)
Decision Under Condition of Certainty (see also Appendix, Section "Mathematics of Decisions Under Certainty")
78(1)
Decision Under Condition of Uncertainty (see also Appendix, Section "Mathematics of Decisions Under Uncertainty")
79(1)
Competitive Decisions: Game Models (see also Appendix "Mathematics of Game Models")
79(1)
Order of Subtask Execution
80(3)
8 Implementing and Evaluating the Action
83(12)
Time to Make a Selection
83(1)
Time to Make an Accurate Movement
84(1)
Continuous Feedback Control (see also Appendix, Section "Mathematics of Continuous Feedback Control")
85(2)
Looking Ahead (Preview Control) (see also Appendix, Section "Mathematics of Preview Control")
87(1)
Delayed Feedback
88(1)
Control by Continuously Updating an Internal Model (see also Appendix, Section "Stepping Through the Kalman Filter System")
88(2)
Expectation of Team Response Time
90(1)
Human Error
91(4)
9 Human-Automation Interaction
95(10)
Human-Automation Allocation
95(1)
Supervisory Control
96(2)
Trading and Sharing
98(3)
Adaptive/Adaptable Control
101(1)
Model-Based Failure Detection
102(3)
10 Mental Models
105(10)
What is a Mental Model?
105(1)
Background of Research on Mental Models
106(2)
ACT-R
108(2)
Lattice Characterization of a Mental Model
110(2)
Neuronal Packet Network as a Model of Understanding
112(1)
Modeling of Aircraft Pilot Decision-Making Under Time Stress
113(1)
Mutual Compatibility of Mental, Display, Control, and Computer Models
114(1)
11 Can Cognitive Engineering Modeling Contribute to Modeling Large-Scale Socio-Technical Systems?
115(14)
Basic Questions
115(1)
What Large-Scale Social Systems are we Talking About?
116(4)
What Models?
120(2)
Potential of Feedback Control Modeling of Large-Scale Societal Systems
122(1)
The STAMP Model for Assessing Errors in Large-Scale Systems
122(1)
Past World Modeling Efforts
123(1)
Toward Broader Participation
124(5)
APPENDIX
129(30)
Mathematics of Fuzzy Logic
129(2)
Mathematics of Statistical Inference from Evidence
131(1)
Mathematics of Information Communication
132(2)
Mathematics of Information Value
134(1)
Mathematics of the Brunswik/Kirlik Perception Model
135(1)
Mathematics of How Often to Sample
136(2)
Mathematics of Signal Detection
138(3)
Research Questions Concerning Mental Workload
141(3)
Behavior Research Issues in Virtual Reality
144(2)
Mathematics of Human Judgment of Utility
146(1)
Mathematics of Decisions Under Certainty
147(2)
Mathematics of Decisions Under Uncertainty
149(1)
Mathematics of Game Models
150(2)
Mathematics of Continuous Feedback Control
152(1)
Mathematics of Preview Control
153(1)
Stepping Through the Kalman Filter System
154(5)
References 159(8)
Index 167
Thomas B. Sheridan is Ford Professor Emeritus in the Aeronautics/Astronautics and Mechanical Engineering departments at the Massachusetts Institute of Technology (MIT), Cambridge, MA, USA. He directed a research laboratory on human-system interaction at MIT. He served as President of both the IEEE Systems, Man and Cybernetics Society and the Human Factors and Ergonomics Society. He is a member of the National Academy of Engineering and author of Humans and Automation (Wiley, 2002).