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Qualitative and quantitative techniques to apply decision analysis to real-world decision problems, supported by sound mathematics, best practices, soft skills, and more
With substantive illustrations based on the authors' personal experiences throughout, Handbook of Decision Analysis 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.
This 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.
Written by a team of highly qualified professionals and academics, Handbook of Decision Analysis includes information on:
Behavioral decision-making insights, decision framing opportunities, collaboration with stakeholders, information assessment, and decision analysis modeling techniques
Principles of value creation through designing alternatives, clear value/risk tradeoffs, and decision implementation
Qualitative and quantitative techniques for each key decision analysis task, as opposed to presenting one technique for all decisions.
Stakeholder analysis, decision hierarchies, and influence diagrams to frame descriptive, predictive, and prescriptive analytics decision problems to ensure implementation success
Handbook of Decision Analysis 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.
Contents
Foreword to the 1st Edition xvii
Foreword to the 2nd Edition xxiii
Preface xxvii
About the Companion Website xxxi
1 Introduction to Decision Analysis and Analytics 1
1.1 Introduction 1
1.2 Decision Analysis is a Social-Technical Process 3
1.3 Decision Analysis Applications 8
1.3.1 Oil and Gas Decision Analysis Success Story - Chevron 10
1.3.2 Pharmaceutical Decision Analysis Success Story - SmithKline Beecham 11
1.3.3 Military and Intelligence Decision Analysis Success Stories 11
1.4 Decision Analysis Practitioners and Professionals 12
1.4.1 Education and Training 12
1.4.2 Decision Analysis Professional Organizations 12
1.4.3 Problem Domain Professional Societies 13
1.4.4 Professional Service 13
1.5 Handbook Overview and Illustrative Examples 14
1.5.1 TechnoMagic New Product Launch 15
1.5.2 Geneptin Personalized Medicine for Breast Cancer 16
1.5.3 Data Center Location and IT Portfolio 17
1.5.4 Roughneck North American Strategy (RNAS) 17
1.6 Summary 17
Key Terms 18
References 18
2 Decision-Making Challenges 21
2.1 Introduction 22
2.2 Human Decision-Making 22
2.3 Decision-Making Challenges 23
2.4 Organizational Decision Processes 24
2.4.1 Culture 24
2.4.2 Impact of Stakeholders 25
2.4.3 Decision Level (Strategic, Tactical, Operational) 26
2.5 Credible Problem Domain Knowledge 28
2.5.1 Dispersion of Knowledge 28
2.5.2 Technical Knowledge - Essential for Credibility 28
2.5.3 Business Knowledge - Essential for Success 29
2.5.4 Role of Experts 29
2.5.5 Limitations of Experts 29
2.6 Behavioral Decision-Analysis Insights 29
2.6.1 Decision Traps and Barriers 30
2.6.2 Cognitive Biases 31
2.7 Two Anecdotes: Long-Term Success and a Temporary Success of Supporting the Human Decision-Making Process 34
2.8 Setting the Human Decision-making Context for the Illustrative Example Problems 35
2.8.1 TechnoMagic New Product Launch 36
2.8.2 Geneptin Personalized Medicine 36
2.8.3 Data Center Decision Problem 36
2.9 Summary 37
Key Terms 37
References 38
3 Foundations of Decision Analysis and Analytics 41
3.1 Introduction 41
3.2 Brief History of the Foundations of Decision Analysis 42
3.3 Five Rules - Theoretical Foundation of Decision Analysis 43
3.4 Scope of Decision Analysis 46
3.5 Decision Analysis and Data Analytics 47
3.6 Taxonomy of Decision Analysis Practice 49
3.6.1 Some DA Terminology 49
3.6.2 Taxonomy Division—Single or Multiple Objectives 50
3.6.2.1 Single-Objective Decision Analysis 50
3.6.2.2 Multiple-Objective Decision Analysis 51
3.6.3 Addressing Value Trade-Offs and Risk Preference Separately or Together? 52
3.6.4 Nonmonetary or Monetary Value Metric? 54
3.6.5 Degree of Simplicity in Multidimensional Value Function 54
3.7 Value-Focused Thinking 55
3.7.1 Four Major VFT Ideas 55
3.7.2 The Benefits of VFT 56
3.8 Summary 57
Key Terms 57
Acknowledgments 58
References 58
4 Decision Analysis Soft Skills 61
4.1 Introduction 62
4.2 Thinking Strategically 62
4.3 Leading Decision Analysis Teams 63
4.4 Managing Decision Analysis Projects 64
4.5 Researching 65
4.6 Interviewing Individuals 65
4.6.1 Before the Interview 66
4.6.2 Schedule/Reschedule the Interview 67
4.6.3 During the Interview 67
4.6.4 After the Interview 67
4.7 Conducting Surveys 68
4.7.1 Preparing an Effective Survey: Determine the Goals, Survey Respondents, and Method for Collecting Survey Data 68
4.7.2 Executing a Survey Instrument: Developing the Survey Questions, Testing, and Distributing the Survey 69
4.8 Facilitating Groups 70
4.8.1 Facilitation Basics 70
4.8.2 Group Processes 72
4.8.2.1 Stages of Group Development 72
4.8.2.2 Planning 73
4.8.2.3 Pulsing 73
4.8.2.4 Pacing 73
4.8.3 Focus Groups 74
4.8.3.1 Preparing for the Focus Group Session 75
4.8.3.2 Executing the Focus Group Session 75
4.9 Aggregating across Experts 75
4.10 Communicating Analysis Insights 76
4.11 Summary 76
Key Terms 77
References 77
5 Use the Appropriate Decision Process 79
5.1 Introduction 79
5.2 What Is a Good Decision? 80
5.2.1 Decision Quality 80
5.2.2 The Six Elements of Decision Quality 80
5.2.3 Intuitive vs. Deliberative Decision-Making 81
5.2.4 Artificial Intelligence-Driven Decision-Making 82
5.3 Selecting the Appropriate Decision Process 83
5.3.1 Tailoring the Decision Process to the Decision 83
5.3.1.1 How Urgent Is the decision? 84
5.3.1.2 How Important Is the Decision? 84
5.3.1.3 Why Is This Decision Difficult to Make? 84
5.3.2 Two Best Practice Decision Processes 84
5.3.2.1 Dialogue Decision Process 84
5.3.2.2 Decision Conferencing 88
5.3.3 Two Flawed Decision Processes 88
5.3.3.1 Strictly Analytical Decision Processes 88
5.3.3.2 Advocacy Decision Processes 89
5.4 Decision Processes in Illustrative Examples 89
5.4.1 TechnoMagic New Product Launch 90
5.4.2 Geneptin Personalized Medicine 90
5.4.3 Data Center Location Decision 91
5.5 Organizational Decision Quality 91
5.6 Decision-Maker's Bill of Rights 92
5.7 Summary 92
Key Terms 93
References 93
6 Frame the Decision Opportunity 95
6.1 Introduction 96
6.2 Declaring a Decision 96
6.3 What Is a Good Decision Frame? 97
6.4 Achieving a Good Decision Frame 98
6.4.1 Vision Statement 99
6.4.2 Issue Raising 100
6.4.3 Categorization of Issues 101
6.4.4 Decision Hierarchy 101
6.4.5 Values and Trade-offs 102
6.4.6 Initial Influence Diagram 102
6.4.7 Decision Schedule and Logistics 103
6.5 Using an Influence Diagram for Decision Framing 103
6.5.1 Introduction to Influence Diagrams 103
6.5.2 Influence Diagram Elements 103
6.5.3 Influence Diagram Rules 106
6.6 Framing the Decision Opportunities for the Illustrative Examples 106
6.6.1 TechnoMagic New Product Launch 106
6.6.2 Geneptin Personalized Medicine 108
6.6.3 Data Center Decision 109
6.7 Using Decision-Analysis Techniques to Frame Analytics Projects 113
6.8 Summary 115
Key Terms 115
References 116
7 Craft the Decision Objectives and Value Measures 117
7.1 Introduction 118
7.2 Shareholder and Stakeholder Value 118
7.2.1 Private Company Example 119
7.2.2 Government Agency Example 119
7.3 Challenges in Identifying Objectives 120
7.4 Identifying the Decision Objectives 121
7.4.1 Questions to Help Identify Decision Objectives 121
7.4.2 How to Get Answers to the Questions 122
7.5 The Financial or Cost Objective 123
7.5.1 Financial Objectives for Private Companies 123
7.5.2 Cost Objective for Public Organizations 123
7.6 Developing Value Measures 124
7.7 Structuring Multiple Objectives 124
7.7.1 Value Hierarchies 125
7.7.2 Techniques for Developing Value Hierarchies 127
7.7.3 Value Hierarchy Best Practices 128
7.7.4 Cautions about Cost, Risk and -ilities Objectives 128
7.8 Illustrative Examples 130
7.8.1 TechnoMagic New Product Decision 130
7.8.2 Geneptin 130
7.8.3 Data Center Location 130
7.9 Summary 132
Key Terms 132
References 133
8 Design Creative Alternatives 135
8.1 Introduction 135
8.2 Characteristics of a Good Set of Alternatives 136
8.3 Obstacles to Creating a Good Set of Alternatives 137
8.4 The Expansive Phase of Creating Alternatives 139
8.5 The Reductive Phase of Creating Alternatives 140
8.6 Improving the Set of Alternatives 143
8.7 Illustrative Examples 143
8.7.1 TechnoMagic New Product Launch 144
8.7.2 Geneptin Personalized Medicine 144
8.7.3 Data Center Location 145
8.8 Summary 146
Key Terms 146
References 146
9 Perform Deterministic Analysis and Develop Insights 149
9.1 Introduction 149
9.2 Planning the Model Using Influence Diagrams 151
9.3 Spreadsheet Software as the Modeling Platform 152
9.3.1 Guidelines for Building a Spreadsheet Decision Model 153
9.3.2 Scenario Analysis 154
9.4 Deterministic Modeling with Net Present Value 154
9.4.1 Net Present Value Calculation 154
9.4.1.1 Explanation of NPV 154
9.4.1.2 Simple NPV Example 155
9.5 Two Illustrative NPV Examples 156
9.5.1 TechnoMagic New Product Launch 156
9.5.1.1 Control Panel Worksheet 157
9.5.1.2 Calculations Worksheet 159
9.5.1.3 Determining the Best Decisions Using Excel's What If Analysis 164
9.5.1.4 Additional Sensitivity Analysis 165
9.5.2 Geneptin NPV Example 168
9.6 Deterministic Modeling Using Multiple-Objective Decision Analysis 170
9.6.1 The Additive Value Function 170
9.6.2 Single-Dimensional Value Functions 171
9.6.3 Swing Weights 174
9.6.4 Swing Weight Matrix 175
9.6.4.1 Consistency Rules 176
9.6.4.2 Assessing Unnormalized Swing Weights 176
9.6.4.3 Calculating Normalized Swing Weights 177
9.6.4.4 Benefits of the Swing Weight Matrix 177
9.6.5 Scoring the Alternatives 177
9.6.6 Deterministic Analysis 179
9.7 Illustrative MODA Problem - Data Center Location 179
9.7.1 Additive Value Model 179
9.7.2 Decision Analysis Software 179
9.7.3 Value Functions 180
9.7.4 Swing Weight Matrix 180
9.7.5 Scoring the Alternatives 182
9.7.6 Implementing the MODA Model in Excel 182
9.7.6.1 Single-Dimensional Value Calculations 185
9.7.6.2 Normalized Swing Weight Calculations 185
9.7.6.3 Alternative Value Calculations 185
9.7.6.4 Value Components 185
9.7.6.5 Value Gaps 189
9.7.6.6 Data Center Life Cycle Costs (LCCs) 189
9.7.6.7 Value vs. Cost 189
9.7.6.8 Waterfall Char 191
9.7.6.9 Sensitivity Analysis 193
9.7.6.10 Value-Focused Thinking 194
9.8 Summary 194
Key Terms 194
References 196
10 Quantify Uncertainty 197
10.1 Introduction 197
10.2 Use the Influence Diagram to Develop Probability Distributions 198
10.3 Probability Assessment with Data 199
10.3.1 General Process 199
10.4 Elicit and Document Subject Matter Expert Assessments 203
10.4.1 Heuristics and Biases 203
10.4.2 Reference Events 204
10.4.3 Assessment Protocol 205
10.4.4 Assessing a Continuous Distribution 207
10.4.5 The Reluctant Expert 208
10.4.6 Making Assumptions to Inform Probabilistic Modeling 209
10.5 Box Assessment Protocols with Artificial Intelligence Tools 210
10.6 Illustrative Examples 211
10.7 Summary 211
Endnotes 211
Key Terms 211
References 212
11 Perform Probabilistic Analysis and Identify Insights 215
11.1 Introduction 216
11.2 Exploration of Uncertainty: Simulation, Decision Trees, and Influence Diagrams 216
11.2.1 Software for Simulation, Decision Trees, and Influence Diagrams 217
11.2.2 Simulation 217
11.2.3 TechnoMagic New Product Launch Monte Carlo Simulation 218
11.2.4 Decision Trees 224
11.2.4.1 Introduction 224
11.2.4.2 Elements of a Decision Tree 224
11.2.4.3 Solving a Decision Tree 225
11.2.4.4 Product Launch Decision 225
11.2.4.5 How to Solve a Decision Tree 226
11.2.4.6 New Product Decision-Tree Solution 226
11.2.4.7 One-Way Sensitivity Analysis 226
11.2.4.8 Two-Way Sensitivity Analysis 227
11.2.4.9 Limitations of Expected Value and Flaw of Averages 227
11.2.4.10 Dominance 229
11.2.5 Influence Diagrams 230
11.2.5.1 Solving the Product Launch Decision with an Influence Diagram 230
11.2.5.2 Converting the Influence Diagram to a Decision Tree 233
11.2.5.3 Risk Profiles 235
11.2.5.4 Comparison of Decision Trees and Influence Diagrams 236
11.2.6 Choosing Between Monte Carlo Simulation and Decision Trees 236
11.2.6.1 Downstream Decisions 236
11.2.6.2 Number of Uncertainties 237
11.2.6.3 Anomalies in the Value Function 237
11.3 Value of Information and Value of Control 238
11.3.1 Introduction 238
11.3.2 Value of Information 238
11.3.3 New Product Problem 238
11.3.4 Perfect Information 238
11.3.5 Expected Value of Control 241
11.3.6 Imperfect Information 241
11.3.7 Comparison of Information and Control 241
11.4 Risk Attitude 242
11.4.1 Delta Property 243
11.4.2 Exponential Utility 243
11.4.3 Assessing Risk Tolerance 243
11.4.4 Calculating Certain Equivalents 245
11.4.5 Evaluating "Small" Risks 245
11.4.6 Going Beyond the Delta Property 246
11.5 Illustrative Examples 246
11.5.1 Geneptin Example 246
11.5.2 Data Center 247
11.6 Summary 248
Key Terms 249
References 250
12 Portfolio Resource Allocation 251
12.1 Introduction to Portfolio Decision Analysis 251
12.2 Socio-technical Challenges with Portfolio Decision Analysis 252
12.3 Portfolio Analysis Using Benefit-Cost Ratios 253
12.4 Net Present Value Portfolio Analysis with Resource Constraints 254
12.4.1 Characteristics of Portfolio Optimization 254
12.4.2 Greedy Algorithm Using Profitability Index and the Efficient Frontier 255
12.4.3 Application to Roughneck North American Strategy Portfolio 258
12.4.4 Portfolio Risk Management 259
12.4.5 Trading off Financial Goals with other Strategic Goals 259
12.5 Multiobjective Portfolio Analysis with Resource Constraints 260
12.5.1 IT Project Portfolio Problem 260
12.5.2 Constraint Precision 263
12.5.3 Integer Optimality 263
12.6 Summary 264
Key Terms 264
References 265
13 Communicate with Decision-Makers and Stakeholders 267
13.1 Introduction 267
13.2 Determining Communication Objectives 269
13.3 Communicating with Senior Leaders 269
13.4 Communicating Decision-Analysis Results 273
13.4.1 Tell the Decision-Maker the Key Insights and Not the Details 273
13.4.2 Communicating Quantitative Information 274
13.4.3 Finding and Telling the Story 275
13.4.4 Best Practices for Presenting Decision-Analysis Results 277
13.4.5 Best Practices for Written Decision-Analysis Results 279
13.5 Communicating Insights in the Illustrative Examples 280
13.5.1 Roughneck North America Strategy 280
13.5.2 Geneptin 280
13.5.3 Data Center Location 281
13.6 Summary 281
Key Terms 282
References 282
14 Enable Decision Implementation 285
14.1 Introduction 285
14.2 Barriers to Involving Decision Implementers 286
14.3 Involving Decision Implementers in the Decision Process 287
14.4 Using Decision Analysis for Decision and Strategy Implementation 289
14.4.1 Using the Decision Model for Decision Implementation 289
14.4.2 Using Decision-Analysis Models to Support Decision Implementation 289
14.4.2.1 Example 1: Gas Plant Implementation 289
14.4.2.2 Example 2: Information Assurance Program Progress 290
14.4.3 Using Decision Analysis to Assess Strategy Implementation 291
14.4.3.1 Example 292
14.5 Illustrative Examples 292
14.5.1 Data Center 292
14.5.2 Rnas 293
14.6 Summary 293
Key Term 293
References 293
15 Summary of Major Themes 295
15.1 Overview 296
15.2 Decision Analysis Helps Answer Important Decision-Making Questions 296
15.3 The Purpose of Decision Analysis Is to Identify and Create Value for Shareholders and Stakeholders 297
15.3.1 Single Objective Value 298
15.3.2 Multiple Objective Value 298
15.3.3 It Is Important to Distinguish Potential Value and Implemented Value 298
15.4 Decision Analysis Is a Sociotechnical Process 298
15.4.1 Social 298
15.4.2 Technical 298
15.5 Decision Analysts Need Decision-Making Knowledge and Soft Skills 298
15.5.1 Decision Analysts Need to Understand Decision-Making Challenges 299
15.5.2 Decision Analysts Must Develop Their Soft Skills 299
15.6 The Decision-Analysis Process Must Be Tailored to the Decision and the Organization 300
15.6.1 Decision Quality 300
15.6.2 Decision Processes 300
15.6.2.1 Decision Conferencing 300
15.6.3 Dialogue Decision Process 300
15.7 Decision Analysis Enables Data-Driven Decision-Making 301
15.8 Decision Analysis Offers Powerful Analytic Tools to Support Decision-Making 301
15.8.1 Decision Framing 302
15.8.2 Identifying Objectives and Value Measures 302
15.8.3 Developing Creative Alternatives 302
15.8.4 Building Decision Models 302
15.8.5 Performing Deterministic Analysis 302
15.8.6 Identifying Uncertainties 303
15.8.7 Performing Probabilistic Analysis 303
15.8.8 Performing Portfolio Resource Allocation 303
15.9 Conclusion 304
Appendix A Probability Theory 305
A. 1 Introduction 305
A. 2 Distinctions and the Clairvoyance Test 305
A. 3 Possibility Tree Representation of a Distinction 306
A. 4 Probability as an Expression of Degree of Belief 307
A. 5 Inferential Notation 307
A. 6 Multiple Distinctions 307
A. 7 Joint, Conditional, and Marginal Probabilities 307
A.8 Calculating Joint Probabilities 308
A.9 Dependent and Independent Probabilities 309
A.10 Reversing Conditional Probabilities - Bayes' Rule 310
A.11 Probability Distributions 311
A.11.1 Summary Statistics for a Probability Distribution 312
A.12 Combining Uncertain Quantities 312
References 313
Appendix B Decision Conferencing 315
B. 1 Introduction 315
B. 2 Decision Conference Process and Format 317
B. 3 Location, Facilities, and Equipment 317
B. 4 Use of Group Processes 318
B. 5 Advantages and Disadvantages 319
B. 6 Best Practices 321
B. 7 Summary 322
Key Terms 322
References 323
Appendix C Resource Allocation with Incremental Benefit/Cost Analysis 325
C. 1 Multiple Objective Portfolio Analysis with Resource Constraints 325
C.. 1 Characteristics of Incremental Benefit/Cost Portfolio Analysis 325
C.1. 2 Algorithm for Incremental Benefit/Cost Portfolio Analysis 326
C..2. 1 Identify the Objective 326
C.1.. 2 Generate Options 326
C.1.2. 3 Assess Costs 328
C.1.2. 4 Assess Benefits 329
C.1.2. 5 Specify Constraints 330
C.1.2. 6 Allocate Resources 331
C.1.2. 7 Perform Sensitivity Analysis 332
C.1. 3 Application to the Data Center Portfolio 332
C.1. 4 Comparison with Portfolio Optimization 337
C.1. 5 Strengths and Weaknesses of Incremental Benefit/Cost Portfolio Analysis 338
C. 2 Summary 338
Key Terms 339
References 340
Appendix D Roughneck North American Strategy 341
D.1 Context 341
D.2 Decision Process 342
D.3 Framing 342
D.4 Objectives and Value Measures 342
D.5 Alternatives 344
D.6 Uncertainty Structuring 344
D.7 Uncertainty Quantification 347
D.8 Evaluation Logic (Spreadsheet Model) 347
D.8.1 Selectors 347
D.8. 2 Inputs Sheet 348
D.8. 3 Strategy Table Sheet 348
D.8. 4 Calculations Sheets 348
D. 9 Probabilistic Analysis 349
D. 10 Real Options 355
D. 11 Portfolio Resource Allocation 358
Reference 360
Index 361
