{"product_id":"modeling-humansystem-interaction-isbn-9781119275268","title":"Modeling Human–System Interaction","description":"\u003cp\u003eThis book presents theories and models to examine how humans interact with complex automated systems, including both empirical and theoretical methods.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eProvides examples of models appropriate to the four stages of human-system interaction\u003c\/li\u003e \u003cli\u003eExamines in detail the philosophical underpinnings and assumptions of modeling\u003c\/li\u003e \u003cli\u003eDiscusses how a model fits into \"doing science\" and the considerations in garnering evidence and arriving at beliefs for the modeled phenomena\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eModeling Human-System Interaction \u003c\/i\u003eis 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.\u003c\/p\u003e \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eIntroduction 1\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Knowledge 5\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGaining New Knowledge 5\u003c\/p\u003e \u003cp\u003eScientific Method: What Is It? 7\u003c\/p\u003e \u003cp\u003eFurther Observations on the Scientific Method 8\u003c\/p\u003e \u003cp\u003eReasoning Logically 10\u003c\/p\u003e \u003cp\u003ePublic (Objective) and Private (Subjective) Knowledge 11\u003c\/p\u003e \u003cp\u003eThe Role of Doubt in Doing Science 11\u003c\/p\u003e \u003cp\u003eEvidence: Its use and Avoidance 12\u003c\/p\u003e \u003cp\u003eMetaphysics and its Relation to Science 12\u003c\/p\u003e \u003cp\u003eObjectivity, Advocacy, and Bias 13\u003c\/p\u003e \u003cp\u003eAnalogy and Metaphor 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 What is a Model? 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining “Model” 17\u003c\/p\u003e \u003cp\u003eModel Attributes: A New Taxonomy 20\u003c\/p\u003e \u003cp\u003eExamples of Models in Terms of the Attributes 25\u003c\/p\u003e \u003cp\u003eWhy Make the Effort to Model? 27\u003c\/p\u003e \u003cp\u003eAttribute Considerations in Making Models Useful 27\u003c\/p\u003e \u003cp\u003eSocial Choice 30\u003c\/p\u003e \u003cp\u003eWhat Models are Not 31\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Important Distinctions in Modeling 33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eObjective and Subjective Models 33\u003c\/p\u003e \u003cp\u003eSimple and Complex Models 35\u003c\/p\u003e \u003cp\u003eDescriptive and Prescriptive (Normative) Models 36\u003c\/p\u003e \u003cp\u003eStatic and Dynamic Models 36\u003c\/p\u003e \u003cp\u003eDeterministic and Probabilistic Models 36\u003c\/p\u003e \u003cp\u003eHierarchy of Abstraction 37\u003c\/p\u003e \u003cp\u003eSome Philosophical Perspectives 38\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Forms of Representation 41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eVerbal Models 41\u003c\/p\u003e \u003cp\u003eGraphs 42\u003c\/p\u003e \u003cp\u003eMaps 44\u003c\/p\u003e \u003cp\u003eSchematic Diagrams 45\u003c\/p\u003e \u003cp\u003eLogic Diagrams 46\u003c\/p\u003e \u003cp\u003eCrisp Versus Fuzzy Logic (see also Appendix, Section “Mathematics of Fuzzy Logic”) 48\u003c\/p\u003e \u003cp\u003eSymbolic Statements and Statistical Inference (see also Appendix, Section “Mathematics of Statistical Inference From Evidence”) 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Acquiring Information 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eInformation Communication (see also Appendix, Section “Mathematics of Information Communication”) 51\u003c\/p\u003e \u003cp\u003eInformation Value (see also Appendix, Section “Mathematics of Information Value”) 53\u003c\/p\u003e \u003cp\u003eLogarithmic‐Like Psychophysical Scales 54\u003c\/p\u003e \u003cp\u003ePerception Process (see also Appendix, Section “Mathematics of the Brunswik\/Kirlik Perception Model”) 54\u003c\/p\u003e \u003cp\u003eAttention 55\u003c\/p\u003e \u003cp\u003eVisual Sampling (see also Appendix, Section “Mathematics of How Often to Sample”) 56\u003c\/p\u003e \u003cp\u003eSignal Detection (see also Appendix, Section “Mathematics of Signal Detection”) 58\u003c\/p\u003e \u003cp\u003eSituation Awareness 59\u003c\/p\u003e \u003cp\u003eMental Workload (see also Appendix, Section “Research Questions Concerning Mental Workload”) 60\u003c\/p\u003e \u003cp\u003eExperiencing What is Virtual: New Demands for Human–System Modeling (see also Appendix Section “Behavior Research Issues in Virtual Reality”) 64\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Analyzing the Information 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTask Analysis 69\u003c\/p\u003e \u003cp\u003eJudgment Calibration 70\u003c\/p\u003e \u003cp\u003eValuation\/Utility (see also Appendix, Section “Mathematics of Human Judgment of Utility”) 72\u003c\/p\u003e \u003cp\u003eRisk and Resilience 73\u003c\/p\u003e \u003cp\u003eDefinition of Risk 73\u003c\/p\u003e \u003cp\u003eMeaning of Resilience 73\u003c\/p\u003e \u003cp\u003eTrust 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Deciding on Action 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat is Achievable 77\u003c\/p\u003e \u003cp\u003eDecision Under Condition of Certainty (see also Appendix, Section “Mathematics of Decisions Under Certainty”) 78\u003c\/p\u003e \u003cp\u003eDecision Under Condition of Uncertainty (see also Appendix, Section “Mathematics of Decisions Under Uncertainty”) 79\u003c\/p\u003e \u003cp\u003eCompetitive Decisions: Game Models (see also Appendix “Mathematics of Game Models”) 79\u003c\/p\u003e \u003cp\u003eOrder of Subtask Execution 80\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Implementing and Evaluating the Action 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTime to Make a Selection 83\u003c\/p\u003e \u003cp\u003eTime to Make an Accurate Movement 84\u003c\/p\u003e \u003cp\u003eContinuous Feedback Control (see also Appendix, Section “Mathematics of Continuous Feedback Control”) 85\u003c\/p\u003e \u003cp\u003eLooking Ahead (Preview Control) (see also Appendix, Section “Mathematics of Preview Control”) 87\u003c\/p\u003e \u003cp\u003eDelayed Feedback 88\u003c\/p\u003e \u003cp\u003eControl by Continuously Updating an Internal Model (see also Appendix, Section “Stepping Through the Kalman Filter System”) 88\u003c\/p\u003e \u003cp\u003eExpectation of Team Response Time 90\u003c\/p\u003e \u003cp\u003eHuman Error 91\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Human–Automation Interaction 95\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHuman–Automation Allocation 95\u003c\/p\u003e \u003cp\u003eSupervisory Control 96\u003c\/p\u003e \u003cp\u003eTrading and Sharing 98\u003c\/p\u003e \u003cp\u003eAdaptive\/Adaptable Control 101\u003c\/p\u003e \u003cp\u003eModel‐Based Failure Detection 102\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Mental Models 105\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat is a Mental Model? 105\u003c\/p\u003e \u003cp\u003eBackground of Research on Mental Models 106\u003c\/p\u003e \u003cp\u003eACT‐R 108\u003c\/p\u003e \u003cp\u003eLattice Characterization of a Mental Model 110\u003c\/p\u003e \u003cp\u003eNeuronal Packet Network as a Model of Understanding 112\u003c\/p\u003e \u003cp\u003eModeling of Aircraft Pilot Decision‐Making Under Time Stress 113\u003c\/p\u003e \u003cp\u003eMutual Compatibility of Mental, Display, Control, and Computer Models 114\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Can Cognitive Engineering Modeling Contribute to Modeling Large‐Scale Socio‐Technical Systems? 115\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBasic Questions 115\u003c\/p\u003e \u003cp\u003eWhat Large‐Scale Social Systems are we Talking About? 116\u003c\/p\u003e \u003cp\u003eWhat Models? 120\u003c\/p\u003e \u003cp\u003ePotential of Feedback Control Modeling of Large‐Scale Societal Systems 122\u003c\/p\u003e \u003cp\u003eThe STAMP Model for Assessing Errors in Large‐Scale Systems 122\u003c\/p\u003e \u003cp\u003ePast World Modeling Efforts 123\u003c\/p\u003e \u003cp\u003eToward Broader Participation 124\u003c\/p\u003e \u003cp\u003eAppendix 129\u003c\/p\u003e \u003cp\u003eMathematics of Fuzzy Logic 129\u003c\/p\u003e \u003cp\u003eMathematics of Statistical Inference from Evidence 131\u003c\/p\u003e \u003cp\u003eMathematics of Information Communication 132\u003c\/p\u003e \u003cp\u003eMathematics of Information Value 134\u003c\/p\u003e \u003cp\u003eMathematics of the Brunswik\/Kirlik Perception Model 135\u003c\/p\u003e \u003cp\u003eMathematics of How Often to Sample 136\u003c\/p\u003e \u003cp\u003eMathematics of Signal Detection 138\u003c\/p\u003e \u003cp\u003eResearch Questions Concerning Mental Workload 141\u003c\/p\u003e \u003cp\u003eBehavior Research Issues in Virtual Reality 144\u003c\/p\u003e \u003cp\u003eMathematics of Human Judgment of Utility 146\u003c\/p\u003e \u003cp\u003eMathematics of Decisions Under Certainty 147\u003c\/p\u003e \u003cp\u003eMathematics of Decisions Under Uncertainty 149\u003c\/p\u003e \u003cp\u003eMathematics of Game Models 150\u003c\/p\u003e \u003cp\u003eMathematics of Continuous Feedback Control 152\u003c\/p\u003e \u003cp\u003eMathematics of Preview Control 153\u003c\/p\u003e \u003cp\u003eStepping Through the Kalman Filter System 154\u003c\/p\u003e \u003cp\u003eReferences 159\u003c\/p\u003e \u003cp\u003eIndex 167\u003c\/p\u003e \u003cp\u003e\u003cb\u003eThomas B. Sheridan\u003c\/b\u003e 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 \u003ci\u003eHumans and Automation \u003c\/i\u003e(Wiley, 2002).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eThis book presents theories and models to examine how humans interact with complex automated systems, including both empirical and theoretical methods\u003c\/b\u003e\u003cb\u003e.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis 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:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eProvides examples of models appropriate to the four stages of human-system interaction\u003c\/li\u003e \u003cli\u003eExamines in detail the philosophical underpinnings and assumptions of modeling\u003c\/li\u003e \u003cli\u003eDiscusses how a model fits into \"doing science\" and the considerations in garnering evidence and arriving at beliefs for the modeled phenomena\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eModeling Human-System Interaction \u003c\/i\u003eis 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989637021925,"sku":"NP9781119275268","price":123.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119275268.jpg?v=1761784905","url":"https:\/\/k12savings.com\/products\/modeling-humansystem-interaction-isbn-9781119275268","provider":"K12savings","version":"1.0","type":"link"}