{"product_id":"handbook-of-decision-analysis-isbn-9781394283880","title":"Handbook of Decision Analysis","description":"\u003cp\u003e\u003cb\u003eQualitative and quantitative techniques to apply decision analysis to real-world decision problems, supported by sound mathematics, best practices, soft skills, and more\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eWith substantive illustrations based on the authors’ personal experiences throughout, \u003ci\u003eHandbook of Decision Analysis\u003c\/i\u003e describes the philosophy, knowledge, science, and art of decision analysis. Key insights from decision analysis applications and behavioral decision analysis research are presented, and numerous decision analysis textbooks, technical books, and research papers are referenced for comprehensive coverage. \u003c\/p\u003e\u003cp\u003eThis book does not introduce new decision analysis mathematical theory, but rather ensures the reader can understand and use the most common mathematics and best practices, allowing them to apply rigorous decision analysis with confidence. The material is supported by examples and solution steps using Microsoft Excel and includes many challenging real-world problems. Given the increase in the availability of data due to the development of products that deliver huge amounts of data, and the development of data science techniques and academic programs, a new theme of this Second Edition is the use of decision analysis techniques with big data and data analytics.  \u003c\/p\u003e\u003cp\u003eWritten by a team of highly qualified professionals and academics, \u003ci\u003eHandbook of Decision Analysis\u003c\/i\u003e includes information on: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eBehavioral decision-making insights, decision framing opportunities, collaboration with stakeholders, information assessment, and decision analysis modeling techniques\u003c\/li\u003e \u003cli\u003ePrinciples of value creation through designing alternatives, clear value\/risk tradeoffs, and decision implementation\u003c\/li\u003e \u003cli\u003eQualitative and quantitative techniques for each key decision analysis task, as opposed to presenting one technique for all decisions.\u003c\/li\u003e \u003cli\u003eStakeholder analysis, decision hierarchies, and influence diagrams to frame descriptive, predictive, and prescriptive analytics decision problems to ensure implementation success\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eHandbook of Decision Analysis\u003c\/i\u003e is a highly valuable textbook, reference, and\/or refresher for students and decision professionals in business, management science, engineering, engineering management, operations management, mathematics, and statistics who want to increase the breadth and depth of their technical and soft skills for success when faced with a professional or personal decision. \u003c\/p\u003e\u003cp\u003eForeword to the 1st Edition xvii\u003c\/p\u003e \u003cp\u003eForeword to the 2nd Edition xxiii\u003c\/p\u003e \u003cp\u003ePreface xxvii\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Decision Analysis and Analytics 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Decision Analysis is a Social-Technical Process 3\u003c\/p\u003e \u003cp\u003e1.3 Decision Analysis Applications 8\u003c\/p\u003e \u003cp\u003e1.3.1 Oil and Gas Decision Analysis Success Story – Chevron 10\u003c\/p\u003e \u003cp\u003e1.3.2 Pharmaceutical Decision Analysis Success Story – SmithKline Beecham 11\u003c\/p\u003e \u003cp\u003e1.3.3 Military and Intelligence Decision Analysis Success Stories 11\u003c\/p\u003e \u003cp\u003e1.4 Decision Analysis Practitioners and Professionals 12\u003c\/p\u003e \u003cp\u003e1.4.1 Education and Training 12\u003c\/p\u003e \u003cp\u003e1.4.2 Decision Analysis Professional Organizations 12\u003c\/p\u003e \u003cp\u003e1.4.3 Problem Domain Professional Societies 13\u003c\/p\u003e \u003cp\u003e1.4.4 Professional Service 13\u003c\/p\u003e \u003cp\u003e1.5 Handbook Overview and Illustrative Examples 14\u003c\/p\u003e \u003cp\u003e1.5.1 TechnoMagic New Product Launch 15\u003c\/p\u003e \u003cp\u003e1.5.2 Geneptin Personalized Medicine for Breast Cancer 16\u003c\/p\u003e \u003cp\u003e1.5.3 Data Center Location and IT Portfolio 17\u003c\/p\u003e \u003cp\u003e1.5.4 Roughneck North American Strategy (RNAS) 17\u003c\/p\u003e \u003cp\u003e1.6 Summary 17\u003c\/p\u003e \u003cp\u003eKey Terms 18\u003c\/p\u003e \u003cp\u003eReferences 18\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Decision-Making Challenges 21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 22\u003c\/p\u003e \u003cp\u003e2.2 Human Decision-Making 22\u003c\/p\u003e \u003cp\u003e2.3 Decision-Making Challenges 23\u003c\/p\u003e \u003cp\u003e2.4 Organizational Decision Processes 24\u003c\/p\u003e \u003cp\u003e2.4.1 Culture 24\u003c\/p\u003e \u003cp\u003e2.4.2 Impact of Stakeholders 25\u003c\/p\u003e \u003cp\u003e2.4.3 Decision Level (Strategic, Tactical, Operational) 26\u003c\/p\u003e \u003cp\u003e2.5 Credible Problem Domain Knowledge 28\u003c\/p\u003e \u003cp\u003e2.5.1 Dispersion of Knowledge 28\u003c\/p\u003e \u003cp\u003e2.5.2 Technical Knowledge – Essential for Credibility 28\u003c\/p\u003e \u003cp\u003e2.5.3 Business Knowledge – Essential for Success 29\u003c\/p\u003e \u003cp\u003e2.5.4 Role of Experts 29\u003c\/p\u003e \u003cp\u003e2.5.5 Limitations of Experts 29\u003c\/p\u003e \u003cp\u003e2.6 Behavioral Decision-Analysis Insights 29\u003c\/p\u003e \u003cp\u003e2.6.1 Decision Traps and Barriers 30\u003c\/p\u003e \u003cp\u003e2.6.2 Cognitive Biases 31\u003c\/p\u003e \u003cp\u003e2.7 Two Anecdotes: Long-Term Success and a Temporary Success of Supporting the Human Decision-Making Process 34\u003c\/p\u003e \u003cp\u003e2.8 Setting the Human Decision-making Context for the Illustrative Example Problems 35\u003c\/p\u003e \u003cp\u003e2.8.1 TechnoMagic New Product Launch 36\u003c\/p\u003e \u003cp\u003e2.8.2 Geneptin Personalized Medicine 36\u003c\/p\u003e \u003cp\u003e2.8.3 Data Center Decision Problem 36\u003c\/p\u003e \u003cp\u003e2.9 Summary 37\u003c\/p\u003e \u003cp\u003eKey Terms 37\u003c\/p\u003e \u003cp\u003eReferences 38\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Foundations of Decision Analysis and Analytics 41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 41\u003c\/p\u003e \u003cp\u003e3.2 Brief History of the Foundations of Decision Analysis 42\u003c\/p\u003e \u003cp\u003e3.3 Five Rules – Theoretical Foundation of Decision Analysis 43\u003c\/p\u003e \u003cp\u003e3.4 Scope of Decision Analysis 46\u003c\/p\u003e \u003cp\u003e3.5 Decision Analysis and Data Analytics 47\u003c\/p\u003e \u003cp\u003e3.6 Taxonomy of Decision Analysis Practice 49\u003c\/p\u003e \u003cp\u003e3.6.1 Some DA Terminology 49\u003c\/p\u003e \u003cp\u003e3.6.2 Taxonomy Division—Single or Multiple Objectives 50\u003c\/p\u003e \u003cp\u003e3.6.2.1 Single-Objective Decision Analysis 50\u003c\/p\u003e \u003cp\u003e3.6.2.2 Multiple-Objective Decision Analysis 51\u003c\/p\u003e \u003cp\u003e3.6.3 Addressing Value Trade-Offs and Risk Preference Separately or Together? 52\u003c\/p\u003e \u003cp\u003e3.6.4 Nonmonetary or Monetary Value Metric? 54\u003c\/p\u003e \u003cp\u003e3.6.5 Degree of Simplicity in Multidimensional Value Function 54\u003c\/p\u003e \u003cp\u003e3.7 Value-Focused Thinking 55\u003c\/p\u003e \u003cp\u003e3.7.1 Four Major VFT Ideas 55\u003c\/p\u003e \u003cp\u003e3.7.2 The Benefits of VFT 56\u003c\/p\u003e \u003cp\u003e3.8 Summary 57\u003c\/p\u003e \u003cp\u003eKey Terms 57\u003c\/p\u003e \u003cp\u003eAcknowledgments 58\u003c\/p\u003e \u003cp\u003eReferences 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Decision Analysis Soft Skills 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 62\u003c\/p\u003e \u003cp\u003e4.2 Thinking Strategically 62\u003c\/p\u003e \u003cp\u003e4.3 Leading Decision Analysis Teams 63\u003c\/p\u003e \u003cp\u003e4.4 Managing Decision Analysis Projects 64\u003c\/p\u003e \u003cp\u003e4.5 Researching 65\u003c\/p\u003e \u003cp\u003e4.6 Interviewing Individuals 65\u003c\/p\u003e \u003cp\u003e4.6.1 Before the Interview 66\u003c\/p\u003e \u003cp\u003e4.6.2 Schedule\/Reschedule the Interview 67\u003c\/p\u003e \u003cp\u003e4.6.3 During the Interview 67\u003c\/p\u003e \u003cp\u003e4.6.4 After the Interview 67\u003c\/p\u003e \u003cp\u003e4.7 Conducting Surveys 68\u003c\/p\u003e \u003cp\u003e4.7.1 Preparing an Effective Survey: Determine the Goals, Survey Respondents, and Method for Collecting Survey Data 68\u003c\/p\u003e \u003cp\u003e4.7.2 Executing a Survey Instrument: Developing the Survey Questions, Testing, and Distributing the Survey 69\u003c\/p\u003e \u003cp\u003e4.8 Facilitating Groups 70\u003c\/p\u003e \u003cp\u003e4.8.1 Facilitation Basics 70\u003c\/p\u003e \u003cp\u003e4.8.2 Group Processes 72\u003c\/p\u003e \u003cp\u003e4.8.2.1 Stages of Group Development 72\u003c\/p\u003e \u003cp\u003e4.8.2.2 Planning 73\u003c\/p\u003e \u003cp\u003e4.8.2.3 Pulsing 73\u003c\/p\u003e \u003cp\u003e4.8.2.4 Pacing 73\u003c\/p\u003e \u003cp\u003e4.8.3 Focus Groups 74\u003c\/p\u003e \u003cp\u003e4.8.3.1 Preparing for the Focus Group Session 75\u003c\/p\u003e \u003cp\u003e4.8.3.2 Executing the Focus Group Session 75\u003c\/p\u003e \u003cp\u003e4.9 Aggregating across Experts 75\u003c\/p\u003e \u003cp\u003e4.10 Communicating Analysis Insights 76\u003c\/p\u003e \u003cp\u003e4.11 Summary 76\u003c\/p\u003e \u003cp\u003eKey Terms 77\u003c\/p\u003e \u003cp\u003eReferences 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Use the Appropriate Decision Process 79\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 79\u003c\/p\u003e \u003cp\u003e5.2 What Is a Good Decision? 80\u003c\/p\u003e \u003cp\u003e5.2.1 Decision Quality 80\u003c\/p\u003e \u003cp\u003e5.2.2 The Six Elements of Decision Quality 80\u003c\/p\u003e \u003cp\u003e5.2.3 Intuitive vs. Deliberative Decision-Making 81\u003c\/p\u003e \u003cp\u003e5.2.4 Artificial Intelligence-Driven Decision-Making 82\u003c\/p\u003e \u003cp\u003e5.3 Selecting the Appropriate Decision Process 83\u003c\/p\u003e \u003cp\u003e5.3.1 Tailoring the Decision Process to the Decision 83\u003c\/p\u003e \u003cp\u003e5.3.1.1 How Urgent Is the decision? 84\u003c\/p\u003e \u003cp\u003e5.3.1.2 How Important Is the Decision? 84\u003c\/p\u003e \u003cp\u003e5.3.1.3 Why Is This Decision Difficult to Make? 84\u003c\/p\u003e \u003cp\u003e5.3.2 Two Best Practice Decision Processes 84\u003c\/p\u003e \u003cp\u003e5.3.2.1 Dialogue Decision Process 84\u003c\/p\u003e \u003cp\u003e5.3.2.2 Decision Conferencing 88\u003c\/p\u003e \u003cp\u003e5.3.3 Two Flawed Decision Processes 88\u003c\/p\u003e \u003cp\u003e5.3.3.1 Strictly Analytical Decision Processes 88\u003c\/p\u003e \u003cp\u003e5.3.3.2 Advocacy Decision Processes 89\u003c\/p\u003e \u003cp\u003e5.4 Decision Processes in Illustrative Examples 89\u003c\/p\u003e \u003cp\u003e5.4.1 TechnoMagic New Product Launch 90\u003c\/p\u003e \u003cp\u003e5.4.2 Geneptin Personalized Medicine 90\u003c\/p\u003e \u003cp\u003e5.4.3 Data Center Location Decision 91\u003c\/p\u003e \u003cp\u003e5.5 Organizational Decision Quality 91\u003c\/p\u003e \u003cp\u003e5.6 Decision-Maker’s Bill of Rights 92\u003c\/p\u003e \u003cp\u003e5.7 Summary 92\u003c\/p\u003e \u003cp\u003eKey Terms 93\u003c\/p\u003e \u003cp\u003eReferences 93\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Frame the Decision Opportunity 95\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 96\u003c\/p\u003e \u003cp\u003e6.2 Declaring a Decision 96\u003c\/p\u003e \u003cp\u003e6.3 What Is a Good Decision Frame? 97\u003c\/p\u003e \u003cp\u003e6.4 Achieving a Good Decision Frame 98\u003c\/p\u003e \u003cp\u003e6.4.1 Vision Statement 99\u003c\/p\u003e \u003cp\u003e6.4.2 Issue Raising 100\u003c\/p\u003e \u003cp\u003e6.4.3 Categorization of Issues 101\u003c\/p\u003e \u003cp\u003e6.4.4 Decision Hierarchy 101\u003c\/p\u003e \u003cp\u003e6.4.5 Values and Trade-offs 102\u003c\/p\u003e \u003cp\u003e6.4.6 Initial Influence Diagram 102\u003c\/p\u003e \u003cp\u003e6.4.7 Decision Schedule and Logistics 103\u003c\/p\u003e \u003cp\u003e6.5 Using an Influence Diagram for Decision Framing 103\u003c\/p\u003e \u003cp\u003e6.5.1 Introduction to Influence Diagrams 103\u003c\/p\u003e \u003cp\u003e6.5.2 Influence Diagram Elements 103\u003c\/p\u003e \u003cp\u003e6.5.3 Influence Diagram Rules 106\u003c\/p\u003e \u003cp\u003e6.6 Framing the Decision Opportunities for the Illustrative Examples 106\u003c\/p\u003e \u003cp\u003e6.6.1 TechnoMagic New Product Launch 106\u003c\/p\u003e \u003cp\u003e6.6.2 Geneptin Personalized Medicine 108\u003c\/p\u003e \u003cp\u003e6.6.3 Data Center Decision 109\u003c\/p\u003e \u003cp\u003e6.7 Using Decision-Analysis Techniques to Frame Analytics Projects 113\u003c\/p\u003e \u003cp\u003e6.8 Summary 115\u003c\/p\u003e \u003cp\u003eKey Terms 115\u003c\/p\u003e \u003cp\u003eReferences 116\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Craft the Decision Objectives and Value Measures 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 118\u003c\/p\u003e \u003cp\u003e7.2 Shareholder and Stakeholder Value 118\u003c\/p\u003e \u003cp\u003e7.2.1 Private Company Example 119\u003c\/p\u003e \u003cp\u003e7.2.2 Government Agency Example 119\u003c\/p\u003e \u003cp\u003e7.3 Challenges in Identifying Objectives 120\u003c\/p\u003e \u003cp\u003e7.4 Identifying the Decision Objectives 121\u003c\/p\u003e \u003cp\u003e7.4.1 Questions to Help Identify Decision Objectives 121\u003c\/p\u003e \u003cp\u003e7.4.2 How to Get Answers to the Questions 122\u003c\/p\u003e \u003cp\u003e7.5 The Financial or Cost Objective 123\u003c\/p\u003e \u003cp\u003e7.5.1 Financial Objectives for Private Companies 123\u003c\/p\u003e \u003cp\u003e7.5.2 Cost Objective for Public Organizations 123\u003c\/p\u003e \u003cp\u003e7.6 Developing Value Measures 124\u003c\/p\u003e \u003cp\u003e7.7 Structuring Multiple Objectives 124\u003c\/p\u003e \u003cp\u003e7.7.1 Value Hierarchies 125\u003c\/p\u003e \u003cp\u003e7.7.2 Techniques for Developing Value Hierarchies 127\u003c\/p\u003e \u003cp\u003e7.7.3 Value Hierarchy Best Practices 128\u003c\/p\u003e \u003cp\u003e7.7.4 Cautions about Cost, Risk and -ilities Objectives 128\u003c\/p\u003e \u003cp\u003e7.8 Illustrative Examples 130\u003c\/p\u003e \u003cp\u003e7.8.1 TechnoMagic New Product Decision 130\u003c\/p\u003e \u003cp\u003e7.8.2 Geneptin 130\u003c\/p\u003e \u003cp\u003e7.8.3 Data Center Location 130\u003c\/p\u003e \u003cp\u003e7.9 Summary 132\u003c\/p\u003e \u003cp\u003eKey Terms 132\u003c\/p\u003e \u003cp\u003eReferences 133\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Design Creative Alternatives 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 135\u003c\/p\u003e \u003cp\u003e8.2 Characteristics of a Good Set of Alternatives 136\u003c\/p\u003e \u003cp\u003e8.3 Obstacles to Creating a Good Set of Alternatives 137\u003c\/p\u003e \u003cp\u003e8.4 The Expansive Phase of Creating Alternatives 139\u003c\/p\u003e \u003cp\u003e8.5 The Reductive Phase of Creating Alternatives 140\u003c\/p\u003e \u003cp\u003e8.6 Improving the Set of Alternatives 143\u003c\/p\u003e \u003cp\u003e8.7 Illustrative Examples 143\u003c\/p\u003e \u003cp\u003e8.7.1 TechnoMagic New Product Launch 144\u003c\/p\u003e \u003cp\u003e8.7.2 Geneptin Personalized Medicine 144\u003c\/p\u003e \u003cp\u003e8.7.3 Data Center Location 145\u003c\/p\u003e \u003cp\u003e8.8 Summary 146\u003c\/p\u003e \u003cp\u003eKey Terms 146\u003c\/p\u003e \u003cp\u003eReferences 146\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Perform Deterministic Analysis and Develop Insights 149\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 149\u003c\/p\u003e \u003cp\u003e9.2 Planning the Model Using Influence Diagrams 151\u003c\/p\u003e \u003cp\u003e9.3 Spreadsheet Software as the Modeling Platform 152\u003c\/p\u003e \u003cp\u003e9.3.1 Guidelines for Building a Spreadsheet Decision Model 153\u003c\/p\u003e \u003cp\u003e9.3.2 Scenario Analysis 154\u003c\/p\u003e \u003cp\u003e9.4 Deterministic Modeling with Net Present Value 154\u003c\/p\u003e \u003cp\u003e9.4.1 Net Present Value Calculation 154\u003c\/p\u003e \u003cp\u003e9.4.1.1 Explanation of NPV 154\u003c\/p\u003e \u003cp\u003e9.4.1.2 Simple NPV Example 155\u003c\/p\u003e \u003cp\u003e9.5 Two Illustrative NPV Examples 156\u003c\/p\u003e \u003cp\u003e9.5.1 TechnoMagic New Product Launch 156\u003c\/p\u003e \u003cp\u003e9.5.1.1 Control Panel Worksheet 157\u003c\/p\u003e \u003cp\u003e9.5.1.2 Calculations Worksheet 159\u003c\/p\u003e \u003cp\u003e9.5.1.3 Determining the Best Decisions Using Excel’s What If Analysis 164\u003c\/p\u003e \u003cp\u003e9.5.1.4 Additional Sensitivity Analysis 165\u003c\/p\u003e \u003cp\u003e9.5.2 Geneptin NPV Example 168\u003c\/p\u003e \u003cp\u003e9.6 Deterministic Modeling Using Multiple-Objective Decision Analysis 170\u003c\/p\u003e \u003cp\u003e9.6.1 The Additive Value Function 170\u003c\/p\u003e \u003cp\u003e9.6.2 Single-Dimensional Value Functions 171\u003c\/p\u003e \u003cp\u003e9.6.3 Swing Weights 174\u003c\/p\u003e \u003cp\u003e9.6.4 Swing Weight Matrix 175\u003c\/p\u003e \u003cp\u003e9.6.4.1 Consistency Rules 176\u003c\/p\u003e \u003cp\u003e9.6.4.2 Assessing Unnormalized Swing Weights 176\u003c\/p\u003e \u003cp\u003e9.6.4.3 Calculating Normalized Swing Weights 177\u003c\/p\u003e \u003cp\u003e9.6.4.4 Benefits of the Swing Weight Matrix 177\u003c\/p\u003e \u003cp\u003e9.6.5 Scoring the Alternatives 177\u003c\/p\u003e \u003cp\u003e9.6.6 Deterministic Analysis 179\u003c\/p\u003e \u003cp\u003e9.7 Illustrative MODA Problem – Data Center Location 179\u003c\/p\u003e \u003cp\u003e9.7.1 Additive Value Model 179\u003c\/p\u003e \u003cp\u003e9.7.2 Decision Analysis Software 179\u003c\/p\u003e \u003cp\u003e9.7.3 Value Functions 180\u003c\/p\u003e \u003cp\u003e9.7.4 Swing Weight Matrix 180\u003c\/p\u003e \u003cp\u003e9.7.5 Scoring the Alternatives 182\u003c\/p\u003e \u003cp\u003e9.7.6 Implementing the MODA Model in Excel 182\u003c\/p\u003e \u003cp\u003e9.7.6.1 Single-Dimensional Value Calculations 185\u003c\/p\u003e \u003cp\u003e9.7.6.2 Normalized Swing Weight Calculations 185\u003c\/p\u003e \u003cp\u003e9.7.6.3 Alternative Value Calculations 185\u003c\/p\u003e \u003cp\u003e9.7.6.4 Value Components 185\u003c\/p\u003e \u003cp\u003e9.7.6.5 Value Gaps 189\u003c\/p\u003e \u003cp\u003e9.7.6.6 Data Center Life Cycle Costs (LCCs) 189\u003c\/p\u003e \u003cp\u003e9.7.6.7 Value vs. Cost 189\u003c\/p\u003e \u003cp\u003e9.7.6.8 Waterfall Char 191\u003c\/p\u003e \u003cp\u003e9.7.6.9 Sensitivity Analysis 193\u003c\/p\u003e \u003cp\u003e9.7.6.10 Value-Focused Thinking 194\u003c\/p\u003e \u003cp\u003e9.8 Summary 194\u003c\/p\u003e \u003cp\u003eKey Terms 194\u003c\/p\u003e \u003cp\u003eReferences 196\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Quantify Uncertainty 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 197\u003c\/p\u003e \u003cp\u003e10.2 Use the Influence Diagram to Develop Probability Distributions 198\u003c\/p\u003e \u003cp\u003e10.3 Probability Assessment with Data 199\u003c\/p\u003e \u003cp\u003e10.3.1 General Process 199\u003c\/p\u003e \u003cp\u003e10.4 Elicit and Document Subject Matter Expert Assessments 203\u003c\/p\u003e \u003cp\u003e10.4.1 Heuristics and Biases 203\u003c\/p\u003e \u003cp\u003e10.4.2 Reference Events 204\u003c\/p\u003e \u003cp\u003e10.4.3 Assessment Protocol 205\u003c\/p\u003e \u003cp\u003e10.4.4 Assessing a Continuous Distribution 207\u003c\/p\u003e \u003cp\u003e10.4.5 The Reluctant Expert 208\u003c\/p\u003e \u003cp\u003e10.4.6 Making Assumptions to Inform Probabilistic Modeling 209\u003c\/p\u003e \u003cp\u003e10.5 Box Assessment Protocols with Artificial Intelligence Tools 210\u003c\/p\u003e \u003cp\u003e10.6 Illustrative Examples 211\u003c\/p\u003e \u003cp\u003e10.7 Summary 211\u003c\/p\u003e \u003cp\u003eEndnotes 211\u003c\/p\u003e \u003cp\u003eKey Terms 211\u003c\/p\u003e \u003cp\u003eReferences 212\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Perform Probabilistic Analysis and Identify Insights 215\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 216\u003c\/p\u003e \u003cp\u003e11.2 Exploration of Uncertainty: Simulation, Decision Trees, and Influence Diagrams 216\u003c\/p\u003e \u003cp\u003e11.2.1 Software for Simulation, Decision Trees, and Influence Diagrams 217\u003c\/p\u003e \u003cp\u003e11.2.2 Simulation 217\u003c\/p\u003e \u003cp\u003e11.2.3 TechnoMagic New Product Launch Monte Carlo Simulation 218\u003c\/p\u003e \u003cp\u003e11.2.4 Decision Trees 224\u003c\/p\u003e \u003cp\u003e11.2.4.1 Introduction 224\u003c\/p\u003e \u003cp\u003e11.2.4.2 Elements of a Decision Tree 224\u003c\/p\u003e \u003cp\u003e11.2.4.3 Solving a Decision Tree 225\u003c\/p\u003e \u003cp\u003e11.2.4.4 Product Launch Decision 225\u003c\/p\u003e \u003cp\u003e11.2.4.5 How to Solve a Decision Tree 226\u003c\/p\u003e \u003cp\u003e11.2.4.6 New Product Decision-Tree Solution 226\u003c\/p\u003e \u003cp\u003e11.2.4.7 One-Way Sensitivity Analysis 226\u003c\/p\u003e \u003cp\u003e11.2.4.8 Two-Way Sensitivity Analysis 227\u003c\/p\u003e \u003cp\u003e11.2.4.9 Limitations of Expected Value and Flaw of Averages 227\u003c\/p\u003e \u003cp\u003e11.2.4.10 Dominance 229\u003c\/p\u003e \u003cp\u003e11.2.5 Influence Diagrams 230\u003c\/p\u003e \u003cp\u003e11.2.5.1 Solving the Product Launch Decision with an Influence Diagram 230\u003c\/p\u003e \u003cp\u003e11.2.5.2 Converting the Influence Diagram to a Decision Tree 233\u003c\/p\u003e \u003cp\u003e11.2.5.3 Risk Profiles 235\u003c\/p\u003e \u003cp\u003e11.2.5.4 Comparison of Decision Trees and Influence Diagrams 236\u003c\/p\u003e \u003cp\u003e11.2.6 Choosing Between Monte Carlo Simulation and Decision Trees 236\u003c\/p\u003e \u003cp\u003e11.2.6.1 Downstream Decisions 236\u003c\/p\u003e \u003cp\u003e11.2.6.2 Number of Uncertainties 237\u003c\/p\u003e \u003cp\u003e11.2.6.3 Anomalies in the Value Function 237\u003c\/p\u003e \u003cp\u003e11.3 Value of Information and Value of Control 238\u003c\/p\u003e \u003cp\u003e11.3.1 Introduction 238\u003c\/p\u003e \u003cp\u003e11.3.2 Value of Information 238\u003c\/p\u003e \u003cp\u003e11.3.3 New Product Problem 238\u003c\/p\u003e \u003cp\u003e11.3.4 Perfect Information 238\u003c\/p\u003e \u003cp\u003e11.3.5 Expected Value of Control 241\u003c\/p\u003e \u003cp\u003e11.3.6 Imperfect Information 241\u003c\/p\u003e \u003cp\u003e11.3.7 Comparison of Information and Control 241\u003c\/p\u003e \u003cp\u003e11.4 Risk Attitude 242\u003c\/p\u003e \u003cp\u003e11.4.1 Delta Property 243\u003c\/p\u003e \u003cp\u003e11.4.2 Exponential Utility 243\u003c\/p\u003e \u003cp\u003e11.4.3 Assessing Risk Tolerance 243\u003c\/p\u003e \u003cp\u003e11.4.4 Calculating Certain Equivalents 245\u003c\/p\u003e \u003cp\u003e11.4.5 Evaluating “Small” Risks 245\u003c\/p\u003e \u003cp\u003e11.4.6 Going Beyond the Delta Property 246\u003c\/p\u003e \u003cp\u003e11.5 Illustrative Examples 246\u003c\/p\u003e \u003cp\u003e11.5.1 Geneptin Example 246\u003c\/p\u003e \u003cp\u003e11.5.2 Data Center 247\u003c\/p\u003e \u003cp\u003e11.6 Summary 248\u003c\/p\u003e \u003cp\u003eKey Terms 249\u003c\/p\u003e \u003cp\u003eReferences 250\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Portfolio Resource Allocation 251\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction to Portfolio Decision Analysis 251\u003c\/p\u003e \u003cp\u003e12.2 Socio-technical Challenges with Portfolio Decision Analysis 252\u003c\/p\u003e \u003cp\u003e12.3 Portfolio Analysis Using Benefit–Cost Ratios 253\u003c\/p\u003e \u003cp\u003e12.4 Net Present Value Portfolio Analysis with Resource Constraints 254\u003c\/p\u003e \u003cp\u003e12.4.1 Characteristics of Portfolio Optimization 254\u003c\/p\u003e \u003cp\u003e12.4.2 Greedy Algorithm Using Profitability Index and the Efficient Frontier 255\u003c\/p\u003e \u003cp\u003e12.4.3 Application to Roughneck North American Strategy Portfolio 258\u003c\/p\u003e \u003cp\u003e12.4.4 Portfolio Risk Management 259\u003c\/p\u003e \u003cp\u003e12.4.5 Trading off Financial Goals with other Strategic Goals 259\u003c\/p\u003e \u003cp\u003e12.5 Multiobjective Portfolio Analysis with Resource Constraints 260\u003c\/p\u003e \u003cp\u003e12.5.1 IT Project Portfolio Problem 260\u003c\/p\u003e \u003cp\u003e12.5.2 Constraint Precision 263\u003c\/p\u003e \u003cp\u003e12.5.3 Integer Optimality 263\u003c\/p\u003e \u003cp\u003e12.6 Summary 264\u003c\/p\u003e \u003cp\u003eKey Terms 264\u003c\/p\u003e \u003cp\u003eReferences 265\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Communicate with Decision-Makers and Stakeholders 267\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 267\u003c\/p\u003e \u003cp\u003e13.2 Determining Communication Objectives 269\u003c\/p\u003e \u003cp\u003e13.3 Communicating with Senior Leaders 269\u003c\/p\u003e \u003cp\u003e13.4 Communicating Decision-Analysis Results 273\u003c\/p\u003e \u003cp\u003e13.4.1 Tell the Decision-Maker the Key Insights and Not the Details 273\u003c\/p\u003e \u003cp\u003e13.4.2 Communicating Quantitative Information 274\u003c\/p\u003e \u003cp\u003e13.4.3 Finding and Telling the Story 275\u003c\/p\u003e \u003cp\u003e13.4.4 Best Practices for Presenting Decision-Analysis Results 277\u003c\/p\u003e \u003cp\u003e13.4.5 Best Practices for Written Decision-Analysis Results 279\u003c\/p\u003e \u003cp\u003e13.5 Communicating Insights in the Illustrative Examples 280\u003c\/p\u003e \u003cp\u003e13.5.1 Roughneck North America Strategy 280\u003c\/p\u003e \u003cp\u003e13.5.2 Geneptin 280\u003c\/p\u003e \u003cp\u003e13.5.3 Data Center Location 281\u003c\/p\u003e \u003cp\u003e13.6 Summary 281\u003c\/p\u003e \u003cp\u003eKey Terms 282\u003c\/p\u003e \u003cp\u003eReferences 282\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Enable Decision Implementation 285\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 285\u003c\/p\u003e \u003cp\u003e14.2 Barriers to Involving Decision Implementers 286\u003c\/p\u003e \u003cp\u003e14.3 Involving Decision Implementers in the Decision Process 287\u003c\/p\u003e \u003cp\u003e14.4 Using Decision Analysis for Decision and Strategy Implementation 289\u003c\/p\u003e \u003cp\u003e14.4.1 Using the Decision Model for Decision Implementation 289\u003c\/p\u003e \u003cp\u003e14.4.2 Using Decision-Analysis Models to Support Decision Implementation 289\u003c\/p\u003e \u003cp\u003e14.4.2.1 Example 1: Gas Plant Implementation 289\u003c\/p\u003e \u003cp\u003e14.4.2.2 Example 2: Information Assurance Program Progress 290\u003c\/p\u003e \u003cp\u003e14.4.3 Using Decision Analysis to Assess Strategy Implementation 291\u003c\/p\u003e \u003cp\u003e14.4.3.1 Example 292\u003c\/p\u003e \u003cp\u003e14.5 Illustrative Examples 292\u003c\/p\u003e \u003cp\u003e14.5.1 Data Center 292\u003c\/p\u003e \u003cp\u003e14.5.2 Rnas 293\u003c\/p\u003e \u003cp\u003e14.6 Summary 293\u003c\/p\u003e \u003cp\u003eKey Term 293\u003c\/p\u003e \u003cp\u003eReferences 293\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Summary of Major Themes 295\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Overview 296\u003c\/p\u003e \u003cp\u003e15.2 Decision Analysis Helps Answer Important Decision-Making Questions 296\u003c\/p\u003e \u003cp\u003e15.3 The Purpose of Decision Analysis Is to Identify and Create Value for Shareholders and Stakeholders 297\u003c\/p\u003e \u003cp\u003e15.3.1 Single Objective Value 298\u003c\/p\u003e \u003cp\u003e15.3.2 Multiple Objective Value 298\u003c\/p\u003e \u003cp\u003e15.3.3 It Is Important to Distinguish Potential Value and Implemented Value 298\u003c\/p\u003e \u003cp\u003e15.4 Decision Analysis Is a Sociotechnical Process 298\u003c\/p\u003e \u003cp\u003e15.4.1 Social 298\u003c\/p\u003e \u003cp\u003e15.4.2 Technical 298\u003c\/p\u003e \u003cp\u003e15.5 Decision Analysts Need Decision-Making Knowledge and Soft Skills 298\u003c\/p\u003e \u003cp\u003e15.5.1 Decision Analysts Need to Understand Decision-Making Challenges 299\u003c\/p\u003e \u003cp\u003e15.5.2 Decision Analysts Must Develop Their Soft Skills 299\u003c\/p\u003e \u003cp\u003e15.6 The Decision-Analysis Process Must Be Tailored to the Decision and the Organization 300\u003c\/p\u003e \u003cp\u003e15.6.1 Decision Quality 300\u003c\/p\u003e \u003cp\u003e15.6.2 Decision Processes 300\u003c\/p\u003e \u003cp\u003e15.6.2.1 Decision Conferencing 300\u003c\/p\u003e \u003cp\u003e15.6.3 Dialogue Decision Process 300\u003c\/p\u003e \u003cp\u003e15.7 Decision Analysis Enables Data-Driven Decision-Making 301\u003c\/p\u003e \u003cp\u003e15.8 Decision Analysis Offers Powerful Analytic Tools to Support Decision-Making 301\u003c\/p\u003e \u003cp\u003e15.8.1 Decision Framing 302\u003c\/p\u003e \u003cp\u003e15.8.2 Identifying Objectives and Value Measures 302\u003c\/p\u003e \u003cp\u003e15.8.3 Developing Creative Alternatives 302\u003c\/p\u003e \u003cp\u003e15.8.4 Building Decision Models 302\u003c\/p\u003e \u003cp\u003e15.8.5 Performing Deterministic Analysis 302\u003c\/p\u003e \u003cp\u003e15.8.6 Identifying Uncertainties 303\u003c\/p\u003e \u003cp\u003e15.8.7 Performing Probabilistic Analysis 303\u003c\/p\u003e \u003cp\u003e15.8.8 Performing Portfolio Resource Allocation 303\u003c\/p\u003e \u003cp\u003e15.9 Conclusion 304\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Probability Theory 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA. 1 Introduction 305\u003c\/p\u003e \u003cp\u003eA. 2 Distinctions and the Clairvoyance Test 305\u003c\/p\u003e \u003cp\u003eA. 3 Possibility Tree Representation of a Distinction 306\u003c\/p\u003e \u003cp\u003eA. 4 Probability as an Expression of Degree of Belief 307\u003c\/p\u003e \u003cp\u003eA. 5 Inferential Notation 307\u003c\/p\u003e \u003cp\u003eA. 6 Multiple Distinctions 307\u003c\/p\u003e \u003cp\u003eA. 7 Joint, Conditional, and Marginal Probabilities 307\u003c\/p\u003e \u003cp\u003eA.8 Calculating Joint Probabilities 308\u003c\/p\u003e \u003cp\u003eA.9 Dependent and Independent Probabilities 309\u003c\/p\u003e \u003cp\u003eA.10 Reversing Conditional Probabilities – Bayes’ Rule 310\u003c\/p\u003e \u003cp\u003eA.11 Probability Distributions 311\u003c\/p\u003e \u003cp\u003eA.11.1 Summary Statistics for a Probability Distribution 312\u003c\/p\u003e \u003cp\u003eA.12 Combining Uncertain Quantities 312\u003c\/p\u003e \u003cp\u003eReferences 313\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Decision Conferencing 315\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB. 1 Introduction 315\u003c\/p\u003e \u003cp\u003eB. 2 Decision Conference Process and Format 317\u003c\/p\u003e \u003cp\u003eB. 3 Location, Facilities, and Equipment 317\u003c\/p\u003e \u003cp\u003eB. 4 Use of Group Processes 318\u003c\/p\u003e \u003cp\u003eB. 5 Advantages and Disadvantages 319\u003c\/p\u003e \u003cp\u003eB. 6 Best Practices 321\u003c\/p\u003e \u003cp\u003eB. 7 Summary 322\u003c\/p\u003e \u003cp\u003eKey Terms 322\u003c\/p\u003e \u003cp\u003eReferences 323\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C Resource Allocation with Incremental Benefit\/Cost Analysis 325\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eC. 1 Multiple Objective Portfolio Analysis with Resource Constraints 325\u003c\/p\u003e \u003cp\u003eC.. 1 Characteristics of Incremental Benefit\/Cost Portfolio Analysis 325\u003c\/p\u003e \u003cp\u003eC.1. 2 Algorithm for Incremental Benefit\/Cost Portfolio Analysis 326\u003c\/p\u003e \u003cp\u003eC..2. 1 Identify the Objective 326\u003c\/p\u003e \u003cp\u003eC.1.. 2 Generate Options 326\u003c\/p\u003e \u003cp\u003eC.1.2. 3 Assess Costs 328\u003c\/p\u003e \u003cp\u003eC.1.2. 4 Assess Benefits 329\u003c\/p\u003e \u003cp\u003eC.1.2. 5 Specify Constraints 330\u003c\/p\u003e \u003cp\u003eC.1.2. 6 Allocate Resources 331\u003c\/p\u003e \u003cp\u003eC.1.2. 7 Perform Sensitivity Analysis 332\u003c\/p\u003e \u003cp\u003eC.1. 3 Application to the Data Center Portfolio 332\u003c\/p\u003e \u003cp\u003eC.1. 4 Comparison with Portfolio Optimization 337\u003c\/p\u003e \u003cp\u003eC.1. 5 Strengths and Weaknesses of Incremental Benefit\/Cost Portfolio Analysis 338\u003c\/p\u003e \u003cp\u003eC. 2 Summary 338\u003c\/p\u003e \u003cp\u003eKey Terms 339\u003c\/p\u003e \u003cp\u003eReferences 340\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix D Roughneck North American Strategy 341\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eD.1 Context 341\u003c\/p\u003e \u003cp\u003eD.2 Decision Process 342\u003c\/p\u003e \u003cp\u003eD.3 Framing 342\u003c\/p\u003e \u003cp\u003eD.4 Objectives and Value Measures 342\u003c\/p\u003e \u003cp\u003eD.5 Alternatives 344\u003c\/p\u003e \u003cp\u003eD.6 Uncertainty Structuring 344\u003c\/p\u003e \u003cp\u003eD.7 Uncertainty Quantification 347\u003c\/p\u003e \u003cp\u003eD.8 Evaluation Logic (Spreadsheet Model) 347\u003c\/p\u003e \u003cp\u003eD.8.1 Selectors 347\u003c\/p\u003e \u003cp\u003eD.8. 2 Inputs Sheet 348\u003c\/p\u003e \u003cp\u003eD.8. 3 Strategy Table Sheet 348\u003c\/p\u003e \u003cp\u003eD.8. 4 Calculations Sheets 348\u003c\/p\u003e \u003cp\u003eD. 9 Probabilistic Analysis 349\u003c\/p\u003e \u003cp\u003eD. 10 Real Options 355\u003c\/p\u003e \u003cp\u003eD. 11 Portfolio Resource Allocation 358\u003c\/p\u003e \u003cp\u003eReference 360\u003c\/p\u003e \u003cp\u003eIndex 361\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eGregory S. Parnell, PhD,\u003c\/b\u003e is a Professor of Practice in the Department of Industrial Engineering at the University of Arkansas in Fayetteville, AR. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eTerry A. Bresnick, MBA,\u003c\/b\u003e is President of Innovative Decision Analysis, and Lecturer in the Department of Industrial Engineering at the University of Arkansas. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eEric R. Johnson, PhD,\u003c\/b\u003e is Director of Decision Science at GSK, where he supports drug development decision making. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eSteven N. Tani, PhD,\u003c\/b\u003e is a retired Partner and Fellow of Strategic Decisions Group. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eEric Specking, PhD,\u003c\/b\u003e is a Principal Engineer for Infinity Labs and a Lecturer in the College of Engineering at the University of Arkansas in Fayetteville, AR.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eQualitative and quantitative techniques to apply decision analysis to real-world decision problems, supported by sound mathematics, best practices, soft skills, and more\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eWith substantive illustrations based on the authors’ personal experiences throughout, \u003ci\u003eHandbook of Decision Analysis\u003c\/i\u003e describes the philosophy, knowledge, science, and art of decision analysis. Key insights from decision analysis applications and behavioral decision analysis research are presented, and numerous decision analysis textbooks, technical books, and research papers are referenced for comprehensive coverage. \u003c\/p\u003e\u003cp\u003eThis book does not introduce new decision analysis mathematical theory, but rather ensures the reader can understand and use the most common mathematics and best practices, allowing them to apply rigorous decision analysis with confidence. The material is supported by examples and solution steps using Microsoft Excel and includes many challenging real-world problems. Given the increase in the availability of data due to the development of products that deliver huge amounts of data, and the development of data science techniques and academic programs, a new theme of this Second Edition is the use of decision analysis techniques with big data and data analytics.  \u003c\/p\u003e\u003cp\u003eWritten by a team of highly qualified professionals and academics, \u003ci\u003eHandbook of Decision Analysis\u003c\/i\u003e includes information on: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eBehavioral decision-making insights, decision framing opportunities, collaboration with stakeholders, information assessment, and decision analysis modeling techniques\u003c\/li\u003e \u003cli\u003ePrinciples of value creation through designing alternatives, clear value\/risk tradeoffs, and decision implementation\u003c\/li\u003e \u003cli\u003eQualitative and quantitative techniques for each key decision analysis task, as opposed to presenting one technique for all decisions.\u003c\/li\u003e \u003cli\u003eStakeholder analysis, decision hierarchies, and influence diagrams to frame descriptive, predictive, and prescriptive analytics decision problems to ensure implementation success\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eHandbook of Decision Analysis\u003c\/i\u003e is a highly valuable textbook, reference, and\/or refresher for students and decision professionals in business, management science, engineering, engineering management, operations management, mathematics, and statistics who want to increase the breadth and depth of their technical and soft skills for success when faced with a professional or personal decision.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989329199333,"sku":"NP9781394283880","price":125.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394283880.jpg?v=1761783688","url":"https:\/\/k12savings.com\/es\/products\/handbook-of-decision-analysis-isbn-9781394283880","provider":"K12savings","version":"1.0","type":"link"}