Modeling Human-System Interaction: Philosophical and Methodological Considerations, with Examples

ISBN-13: 9781119275268 / Angielski / Twarda / 2017 / 192 str.

Thomas B. Sheridan

Opis

This book presents theories and models to examine how humans interact with complex automated systems, including both empirical and theoretical methods.

• Provides examples of models appropriate to the four stages of human-system interaction
• Examines in detail the philosophical underpinnings and assumptions of modeling
• Discusses how a model fits into -doing science- and the considerations in garnering evidence and arriving at beliefs for the modeled phenomena

Modeling Human-System Interaction is a reference for professionals in industry, academia and government who are researching, designing and implementing human-technology systems in transportation, communication, manufacturing, energy, and health care sectors.

Spis treści

Preface xi

Introduction 1

1 Knowledge 5

Gaining New Knowledge 5

Scientific Method: What Is It? 7

Further Observations on the Scientific Method 8

Reasoning Logically 10

Public (Objective) and Private (Subjective) Knowledge 11

The Role of Doubt in Doing Science 11

Evidence: Its use and Avoidance 12

Metaphysics and its Relation to Science 12

Objectivity, Advocacy, and Bias 13

Analogy and Metaphor 14

2 What is a Model? 17

Defining Model 17

Model Attributes: A New Taxonomy 20

Examples of Models in Terms of the Attributes 25

Why Make the Effort to Model? 27

Attribute Considerations in Making Models Useful 27

Social Choice 30

What Models are Not 31

3 Important Distinctions in Modeling 33

Objective and Subjective Models 33

Simple and Complex Models 35

Descriptive and Prescriptive (Normative) Models 36

Static and Dynamic Models 36

Deterministic and Probabilistic Models 36

Hierarchy of Abstraction 37

Some Philosophical Perspectives 38

4 Forms of Representation 41

Verbal Models 41

Graphs 42

Maps 44

Schematic Diagrams 45

Logic Diagrams 46

Crisp Versus Fuzzy Logic (see also Appendix, Section Mathematics of Fuzzy Logic ) 48

Symbolic Statements and Statistical Inference (see also Appendix, Section Mathematics of Statistical Inference From Evidence ) 50

5 Acquiring Information 51

Information Communication (see also Appendix, Section Mathematics of Information Communication ) 51

Information Value (see also Appendix, Section Mathematics of Information Value ) 53

Logarithmic Like Psychophysical Scales 54

Perception Process (see also Appendix, Section Mathematics of the Brunswik/Kirlik Perception Model ) 54

Attention 55

Visual Sampling (see also Appendix, Section Mathematics of How Often to Sample ) 56

Signal Detection (see also Appendix, Section Mathematics of Signal Detection ) 58

Situation Awareness 59

Mental Workload (see also Appendix, Section Research Questions Concerning Mental Workload ) 60

Experiencing What is Virtual: New Demands for Human System Modeling (see also Appendix, Section Behavior Research Issues in Virtual Reality ) 64

6 Analyzing the Information 69

Task Analysis 69

Judgment Calibration 70

Valuation/Utility (see also Appendix, Section Mathematics of Human Judgment of Utility ) 72

Risk and Resilience 73

Definition of Risk 73

Meaning of Resilience 73

Trust 75

7 Deciding on Action 77

What is Achievable 77

Decision Under Condition of Certainty (see also Appendix, Section Mathematics of Decisions Under Certainty ) 78

Decision Under Condition of Uncertainty (see also Appendix, Section Mathematics of Decisions Under Uncertainty ) 79

Competitive Decisions: Game Models (see also Appendix Mathematics of Game Models ) 79

Order of Subtask Execution 80

8 Implementing and Evaluating the Action 83

Time to Make a Selection 83

Time to Make an Accurate Movement 84

Continuous Feedback Control (see also Appendix, Section Mathematics of Continuous Feedback Control ) 85

Looking Ahead (Preview Control) (see also Appendix, Section Mathematics of Preview Control ) 87

Delayed Feedback 88

Control by Continuously Updating an Internal Model (see also Appendix, Section Stepping Through the Kalman Filter System ) 88

Expectation of Team Response Time 90

Human Error 91

9 Human Automation Interaction 95

Human Automation Allocation 95

Supervisory Control 96

Trading and Sharing 98

Adaptive/Adaptable Control 101

Model Based Failure Detection 102

10 Mental Models 105

What is a Mental Model? 105

Background of Research on Mental Models 106

Act R 108

Lattice Characterization of a Mental Model 110

Neuronal Packet Network as a Model of Understanding 112

Modeling of Aircraft Pilot Decision Making Under Time Stress 113

Mutual Compatibility of Mental, Display, Control, and Computer

Models 114

11 Can Cognitive Engineering Modeling Contribute to Modeling Large Scale Socio Technical Systems? 115

Basic Questions 115

What Large Scale Social Systems are we Talking About? 116

What Models? 120

Potential of Feedback Control Modeling of Large Scale Societal Systems 122

The STAMP Model for Assessing Errors in Large Scale Systems 122

Past World Modeling Efforts 123

Toward Broader Participation 124

APPENDIX 129

Mathematics of Fuzzy Logic 129

Mathematics of Statistical Inference from Evidence 131

Mathematics of Information Communication 132

Mathematics of Information Value 134

Mathematics of the Brunswik/Kirlik Perception Model 135

Mathematics of How Often to Sample 136

Mathematics of Signal Detection 138

Research Questions Concerning Mental Workload 141

Behavior Research Issues in Virtual Reality 144

Mathematics of Human Judgment of Utility 146

Mathematics of Decisions Under Certainty 147

Mathematics of Decisions Under Uncertainty 149

Mathematics of Game Models 150

Mathematics of Continuous Feedback Control 152

Mathematics of Preview Control 153

Stepping Through the Kalman Filter System 154

REFERENCES 159

Index 167

O autorze

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).

Blurb

This book presents theories and models to examine how humans interact with complex automated systems, including both empirical and theoretical methods.

This book provides an overview of the reasons for modeling in the human–technology system, including the various pitfalls and difficulties.  Scientific modeling has become a critical part of research and design. This is especially true in systems where humans and technology interact, where cognitive and physical variables come together. The book discusses models and tradeoffs for large–scale societal systems. Other topics the book covers include the considerations in rational modeling in any field of science or engineering, the various forms of representation that a model can take, and the most important elements of the model, with references cited for further reading. The authors identify several categories of major societal issues, particularly with respect to analyzing trade–off relationships. In addition, this book:

• Provides examples of models appropriate to the four stages of human–system interaction
• Examines in detail the philosophical underpinnings and assumptions of modeling
• Discusses how a model fits into "doing science" and the considerations in garnering evidence and arriving at beliefs for the modeled phenomena

Modeling Human–System Interaction is a reference for professionals in industry, academia, and government who are researching, designing, and implementing human–technology systems in transportation, communication, manufacturing, energy, and health care sectors.