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

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.

Preface

Introduction

Chapter 1. Knowledge

1.1. Gaining New Knowledge

1.2. Scientific Method: What Is It?

1.3. Further Observations on the Scientific Method

1.4. Reasoning Logically

1.5. Public (Objective) and Private (Subjective) Knowledge

1.6. The Role of Doubt in Doing Science

1.7. Evidence: Its Use and Avoidance

1.8. Metaphysics and Its Relation to Science

1.9. Objectivity, Advocacy and Bias

1.10. Analogy and Metaphor

Chapter 2. What is a Model?

1.11. Defining Model

1.12. Model Attributes: A New Taxonomy

1.13. Examples of Models In Terms of the Attributes

1.14. Why Make The Effort To Model?

1.15. Attribute Considerations in Making Models Useful

1.16. Social Choice

1.17. What Models Are Not

Chapter 3. Important distinctions in modeling

1.18. Objective and Subjective Models

1.19. Simple and Complex Models

1.20. Descriptive and Prescriptive (Normative) Models

1.21. Static and Dynamic Models

1.22. Deterministic and Probabilistic Models

1.23. Hierarchy of Abstraction

1.24. Some Philosophical Perspectives

Chapter 4. Forms of Representation

1.25. Verbal Models

1.26. Graphs

1.27. Maps

1.28. Schematic Diagrams

1.29. Logic Diagrams

1.30. Crisp Versus Fuzzy Logic

1.31. Symbolic Statements and Statistical Inference

Chapter 5. Acquiring information

1.32. Information Communication

1.33. Information Value

1.34. Logarithmic–Like Psychophysical Scales

1.35. Perception Process

1.36. Attention

1.37. Visual Sampling

1.38. Signal Detection

1.39. Situation Awareness

1.40. Mental Workload

1.41. Experiencing What Is Virtual; New Demands for Modeling

Chapter 6. Analyzing the information

1.42. Task Analysis

1.43. Judgment Calibration

1.44. Valuation/Utility

1.45. Risk and Resilience

1.46. Trust

Chapter 7. Deciding on Action

1.47. What Is Achievable

1.48. Decision under Condition of Certainty

1.49. Decision under Condition of Uncertainty

1.50. Competitive Decisions: Game Models

1.51. Order of Subtask Execution

Chapter 8. Implementing and evaluating the action

1.52. Time to Make a Selection

1.53. Time to Make an Accurate Movement

1.54. Continuous Feedback Control

1.55. Looking Ahead (Preview Control)

1.56. Delayed Feedback

1.57. Control by Continuously Updating an Internal Model

1.58. Expectation of Team Response Time

1.59. Human Error

Chapter 9. Human–automation interaction

1.60. Human–Automation Allocation

1.61. Supervisory Control

1.62. Trading and Sharing

1.63. Adaptive/Adaptable Control

1.64. Model–Based Failure Detection

Chapter 10. Mental models

1.65. What Is A Mental Model?

1.66. Background of Research on Mental Models

1.67. Act–R

1.68. Lattice Characterization of a Mental Model

1.69. Neuronal Packet Network as a Model of Understanding

1.70. Modeling of Aircraft Pilot Decision–Making under Time Stress

1.71. Mutual Compatibility of Mental, Display, Control and Computer Models

Chapter 11. Can cognitive engineering modeling contribute to modeling large–scale sociotechnical systems?

1.72. Basic Questions

1.73. What Large–Scale Systems Are We Talking About?

1.74. What Models?

1.75. Potential of Feedback Control Modeling Of Large Scale Societal Systems

1.76. The Stamp Model for Assessing Errors in Large Scale Systems

1.77. Past World Modeling Efforts

1.78. Toward Broader Participation

Appendices

A1. Mathematics of Fuzzy Logic

A2. Mathematics of Statistical Inference from Evidence

A3. Mathematics of Information Communication

A4. Mathematics of Information Value

A5. Mathematics of the Brunswik/Kirlik Perception Model

A6. Mathematics of How Often to Sample

A7. Mathematics of Signal Detection

A8. Research Questions Concerning Mental Workload

A9. Behavior Research Issues In VR

A10. Mathematics of Human Judgment of Utility

A11. Mathematics of Decisions under Certainty

A12. Mathematics of Decisions under Uncertainty

A13. Mathematics of Game Models

A14. Mathematics of Continuous Feedback Control

A15. Mathematics of Preview Control

A16. Stepping Through the Kalman Filter System

References

Index

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).