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. 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. INDICE: 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

• ISBN: 978-1-119-27526-8
• Editorial: Wiley–Blackwell
• Encuadernacion: Cartoné
• Páginas: 192
• Fecha Publicación: 07/03/2017
• Nº Volúmenes: 1
• Idioma: Inglés