Handbook of Decision Analysis E-Book

Gregory S. Parnell

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Wiley Series in Operations Research and Management Science
• Sprache: Englisch

Seitenzahl: 757

Veröffentlichungsjahr: 2025

Beschreibung

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.

Leseprobe

Table of Contents

Cover

Table of Contents

Title Page

Copyright

Foreword to the 1st Edition

This Handbook Is Timely

Decision Professionals: The Practitioner Perspective

Our Profession

The Biggest Challenge

Notes

Foreword to the 2nd Edition

Ubiquitous Computer Power

Big Data on the Internet

Artificial Intelligence

Preface

About the Companion Website

1 Introduction to Decision Analysis and Analytics

1.1 Introduction

1.2 Decision Analysis is a Social‐Technical Process

1.3 Decision Analysis Applications

1.4 Decision Analysis Practitioners and Professionals

1.5 Handbook Overview and Illustrative Examples

1.6 Summary

Key Terms

References

Notes

2 Decision‐Making Challenges

2.1 Introduction

2.2 Human Decision‐Making

2.3 Decision‐Making Challenges

2.4 Organizational Decision Processes

2.5 Credible Problem Domain Knowledge

2.6 Behavioral Decision‐Analysis Insights

2.7 Two Anecdotes: Long‐Term Success and a Temporary Success of Supporting the Human Decision‐Making Process

2.8 Setting the Human Decision‐making Context for the Illustrative Example Problems

2.9 Summary

Key Terms

References

3 Foundations of Decision Analysis and Analytics

3.1 Introduction

3.2 Brief History of the Foundations of Decision Analysis

3.3 Five Rules – Theoretical Foundation of Decision Analysis

3.4 Scope of Decision Analysis

3.5 Decision Analysis and Data Analytics

3.6 Taxonomy of Decision Analysis Practice

3.7 Value‐Focused Thinking

3.8 Summary

Key Terms

Acknowledgments

References

Notes

4 Decision Analysis Soft Skills

4.1 Introduction

4.2 Thinking Strategically

4.3 Leading Decision Analysis Teams

4.4 Managing Decision Analysis Projects

4.5 Researching

4.6 Interviewing Individuals

4.7 Conducting Surveys

4.8 Facilitating Groups

4.9 Aggregating across Experts

4.10 Communicating Analysis Insights

4.11 Summary

Key Terms

References

5 Use the Appropriate Decision Process

5.1 Introduction

5.2 What Is a Good Decision?

5.3 Selecting the Appropriate Decision Process

5.4 Decision Processes in Illustrative Examples

5.5 Organizational Decision Quality

5.6 Decision‐Maker's Bill of Rights

5.7 Summary

Key Terms

References

Note

6 Frame the Decision Opportunity

6.1 Introduction

6.2 Declaring a Decision

6.3 What Is a Good Decision Frame?

6.4 Achieving a Good Decision Frame

6.5 Using an Influence Diagram for Decision Framing

6.6 Framing the Decision Opportunities for the Illustrative Examples

6.7 Using Decision‐Analysis Techniques to Frame Analytics Projects

6.8 Summary

Key Terms

References

Notes

7 Craft the Decision Objectives and Value Measures

7.1 Introduction

7.2 Shareholder and Stakeholder Value

7.3 Challenges in Identifying Objectives

7.4 Identifying the Decision Objectives

7.5 The Financial or Cost Objective

7.6 Developing Value Measures

7.7 Structuring Multiple Objectives

7.8 Illustrative Examples

7.9 Summary

Key Terms

References

Notes

8 Design Creative Alternatives

8.1 Introduction

8.2 Characteristics of a Good Set of Alternatives

8.3 Obstacles to Creating a Good Set of Alternatives

8.4 The Expansive Phase of Creating Alternatives

8.5 The Reductive Phase of Creating Alternatives

8.6 Improving the Set of Alternatives

8.7 Illustrative Examples

8.8 Summary

Key Terms

References

Notes

9 Perform Deterministic Analysis and Develop Insights

9.1 Introduction

9.2 Planning the Model Using Influence Diagrams

9.3 Spreadsheet Software as the Modeling Platform

9.4 Deterministic Modeling with Net Present Value

9.5 Two Illustrative NPV Examples

9.6 Deterministic Modeling Using Multiple‐Objective Decision Analysis

9.7 Illustrative MODA Problem – Data Center Location

9.8 Summary

Key Terms

References

Notes

10 Quantify Uncertainty

10.1 Introduction

10.2 Use the Influence Diagram to Develop Probability Distributions

10.3 Probability Assessment with Data

10.4 Elicit and Document Subject Matter Expert Assessments

10.5 Box Assessment Protocols with Artificial Intelligence Tools

10.6 Illustrative Examples

10.7 Summary

Endnotes

Key Terms

References

Notes

11 Perform Probabilistic Analysis and Identify Insights

11.1 Introduction

11.2 Exploration of Uncertainty: Simulation, Decision Trees, and Influence Diagrams

11.3 Value of Information and Value of Control

11.4 Risk Attitude

11.5 Illustrative Examples

11.6 Summary

Key Terms

References

Notes

12 Portfolio Resource Allocation

12.1 Introduction to Portfolio Decision Analysis

12.2 Socio‐technical Challenges with Portfolio Decision Analysis

12.3 Portfolio Analysis Using Benefit–Cost Ratios

12.4 Net Present Value Portfolio Analysis with Resource Constraints

12.5 Multiobjective Portfolio Analysis with Resource Constraints

12.6 Summary

Key Terms

References

Notes

13 Communicate with Decision‐Makers and Stakeholders

13.1 Introduction

13.2 Determining Communication Objectives

13.3 Communicating with Senior Leaders

13.4 Communicating Decision‐Analysis Results

13.5 Communicating Insights in the Illustrative Examples

13.6 Summary

Key Terms

References

Notes

14 Enable Decision Implementation

14.1 Introduction

14.2 Barriers to Involving Decision Implementers

14.3 Involving Decision Implementers in the Decision Process

14.4 Using Decision Analysis for Decision and Strategy Implementation

14.5 Illustrative Examples

14.6 Summary

Key Term

References

15 Summary of Major Themes

15.1 Overview

15.2 Decision Analysis Helps Answer Important Decision‐Making Questions

15.3 The Purpose of Decision Analysis Is to Identify and Create Value for Shareholders and Stakeholders

15.4 Decision Analysis Is a Sociotechnical Process

15.5 Decision Analysts Need Decision‐Making Knowledge and Soft Skills

15.6 The Decision‐Analysis Process Must Be Tailored to the Decision and the Organization

15.7 Decision Analysis Enables Data‐Driven Decision‐Making

15.8 Decision Analysis Offers Powerful Analytic Tools to Support Decision‐Making

15.9 Conclusion

Appendix A: Probability Theory

A.1 Introduction

A.2 Distinctions and the Clairvoyance Test

A.3 Possibility Tree Representation of a Distinction

A.4 Probability as an Expression of Degree of Belief

A.5 Inferential Notation

A.6 Multiple Distinctions

A.7 Joint, Conditional, and Marginal Probabilities

A.8 Calculating Joint Probabilities

A.9 Dependent and Independent Probabilities

A.10 Reversing Conditional Probabilities – Bayes' Rule

A.11 Probability Distributions

A.12 Combining Uncertain Quantities

References

Note

Appendix B: Decision Conferencing

B.1 Introduction

B.2 Decision Conference Process and Format

B.3 Location, Facilities, and Equipment

B.4 Use of Group Processes

B.5 Advantages and Disadvantages

B.6 Best Practices

B.7 Summary

Key Terms

References

Notes

Appendix C: Resource Allocation with Incremental Benefit/Cost Analysis

C.1 Multiple Objective Portfolio Analysis with Resource Constraints

C.2 Summary

Key Terms

References

Notes

Appendix D: Roughneck North American Strategy

D.1 Context

D.2 Decision Process

D.3 Framing

D.4 Objectives and Value Measures

D.5 Alternatives

D.6 Uncertainty Structuring

D.7 Uncertainty Quantification

D.8 Evaluation Logic (Spreadsheet Model)

D.9 Probabilistic Analysis

D.10 Real Options

D.11 Portfolio Resource Allocation

Reference

Notes

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 List of Technical Products and Soft Skills.

Table 1.2 Comparison of Three Decision Analysis Application Areas.

Table 1.3 Location of Illustrative Examples.

Chapter 2

Table 2.1 Techniques for Stakeholder Analysis.

Chapter 4

Table 4.1 Advantages and Disadvantages of Surveys.

Chapter 5

Table 5.1 Fitting the Process to the Decision.

Chapter 6

Table 6.1 Concern List by Stakeholder.

Table 6.2 Stakeholder Issue Identification Matrix.

Chapter 7

Table 7.1 Preference for Types of Value Measure.

Chapter 8

Table 8.1 Example Strategy Table.

Table 8.2 Defining Two Alternatives in a Strategy Table.

Table 8.3 Strategy Table in Matrix Format.

Table 8.4 Nested Strategy Tables.

Table 8.5 Data Center Strategy Generation Table.

Chapter 9

Table 9.1 Deterministic Results for Manufacturing Technology Example.

Table 9.2 Total Value for Manufacturing Technology Example.

Table 9.3 The Elements of the Swing Weight Matrix.

Table 9.4 Data Center Single‐Dimensional Value Functions.

Table 9.5 Data Center Swing Weight Matrix.

Table 9.6 Data Center Scores on Each Value Measure.

Table 9.7 Data Center Single‐Dimensional Value Calculations for Each Value M...

Table 9.8 Data Center Normalized Swing Weights.

Table 9.9 Data Center Weighted Value and Total Value Calculations.

Table 9.10 Life Cycle Cost of the Data Center Alternatives.

Table 9.11 Data Center Life Cycle Cost and Value for Each Alternative.

Chapter 10

Table 10.1 Popular Distributions and Parameters.

Chapter 12

Table 12.1 RNAS Portfolio Data ($M).

Chapter 13

Table 13.1 Decision Team Communication Objectives and Stakeholder Objectives...

Chapter 14

Table 14.1 Decision Implementation Roles and Questions.

Appendix A

Table A.1 Equalities When Combining Uncertain Quantities.

Appendix B

Table B.1 Advantages and Disadvantages.

Appendix C

Table C.1 Projects Requesting Funding.

Table C.2 MODA Value Scales.

Table C.3 Values for Projects.

Table C.4 Project Order‐of‐Buy.

Table C.5 Best Portfolio for $450M.

Appendix D

Table D.1 RNAS Strategy Table.

Table D.2 RNAS E&P Profit and Loss Statement ‐ Growth Strategy (Amounts in $...

Table D.3 RNAS Value Components ($B, Present Value).

Table D.4 Expanded Strategy Table.

Table D.5 Two‐Way Sensitivity Analysis of RNAS Hybrid Strategies to Gas Pric...

Table D.6 RNAS Portfolio Metrics ($B).

List of Illustrations

Foreword to the 1st Edition

Figure F.1 Two Dimensions of Competence.

Preface

Figure P.1 Chapter organization of the

Decision Analysis Handbook

.

Chapter 1

Figure 1.1 Decision Analysis Process.

Figure 1.2 Generic Analytics Process Created from ABoK (Keenan, et al., 2022...

Figure 1.3 Combined Decision Analysis and Generic Analytics Processes.

Chapter 2

Figure 2.1 Dimensions of Decision Complexity

Chapter 3

Figure 3.1 The Scope of Decision Analysis.

Figure 3.2 A Taxonomy of Decision Analysis Practice.

Figure 3.3 Single‐Objective Decision Analysis.

Figure 3.4 Two Approaches to Multiple Objective Decision Analysis.

Figure 3.5 Example of Indifference Curves.

Figure 3.6 Benefits of Value‐Focused Thinking

Chapter 4

Figure 4.1  Divergent and convergent thinking.

Chapter 5

Figure 5.1 Six Elements of decision quality.

Figure 5.2 Suggested Prescription for Resolving Decisions.

Figure 5.3 The dialogue decision process.

Figure 5.4 Combined Decision Analysis and Generic Analytics Processes (Figur...

Figure 5.5 Systems Decision Process.

Figure 5.6 Strictly Analytical Process.

Figure 5.7 Advocacy Process.

Figure 5.8 The Geneptin dialogue decision process.

Chapter 6

Figure 6.1 Example Vision Statement.

Figure 6.2 Format of the Decision Hierarchy.

Figure 6.3 Elements of an Influence Diagram.

Figure 6.4 Types of Influences.

Figure 6.5 Decision Hierarchy for New Product Launch.

Figure 6.6 New Product Launch Influence Diagram.

Figure 6.7 Geneptin Decision Hierarchy.

Figure 6.8 Geneptin Influence Diagram.

Figure 6.9 Data Center Location Decision Hierarchy.

Figure 6.10 Data Center Location Decision Hierarchy.

Figure 6.11 Fort Carson Weather Warning Decision Hierarchy.

Figure 6.12 Fort Carson Weather Warning Decision Influence Diagram.

Chapter 7

Figure 7.1 Objectives Hierarchy for Car Purchase.

Figure 7.2 Functional Value Hierarchy for Car Purchase.

Figure 7.3 Comparison of Objectives and Functional Objectives Hierarchy.

Figure 7.4 Data Center Functional Value Hierarchy.

Chapter 8

Figure 8.1 Creating Alternatives.

Figure 8.2 Geneptin Strategy Table.

Chapter 9

Figure 9.1 NPV Example with Excel Formulas.

Figure 9.2 New Product Launch Influence Diagram.

Figure 9.3 Parameter Screenshot from the Excel Model.

Figure 9.4 Parameter Screenshot with Formulas Shown from the Excel Model.

Figure 9.5 Decisions Portion of the Excel Model.

Figure 9.6 Objective of the Excel Model (NPV).

Figure 9.7 Calculations Worksheet Steps 1–3 Screenshot.

Figure 9.8 Calculations Worksheet Steps 1‐3 Screenshot with Formulas.

Figure 9.9 Calculations Worksheet Steps 4–6 Screenshot.

Figure 9.10 Calculations Worksheet Steps 4–6 Screenshot with Formulas.

Figure 9.11 Example Using the Normalized Exponential Function.

Figure 9.12 Calculations Worksheet Step 7 Screenshot.

Figure 9.13 Calculations Worksheet Step 7 Screenshot with Formulas.

Figure 9.14 Calculations Worksheet Step 8 Screenshot.

Figure 9.15 Calculations Worksheet Step 8 Screenshot with Formula.

Figure 9.16 Calculations Worksheet Step 9 Screenshot.

Figure 9.17 Calculations Worksheet Step 9 Screenshot with Formulas Shown.

Figure 9.18 Calculations Worksheet Step 10 Screenshot.

Figure 9.19 Calculations Worksheet Step 10 Screenshot with Formulas.

Figure 9.20 What If Analysis Screenshot.

Figure 9.21 What If Analysis Screenshot with Formulas Shown.

Figure 9.22 What If Analysis with Data Table Inputs Screenshot.

Figure 9.23 3D Plot of What‐If Analysis from Figure 9.22.

Figure 9.24 One‐Way Sensitivity Analysis on Product Features Parameter.

Figure 9.25 One‐Way Sensitivity Analysis Product Features Parameter with For...

Figure 9.26 One‐Way Sensitivity Analysis Product Features Parameters Data Ta...

Figure 9.27 Summary Table for One‐Way Sensitivity Analysis.

Figure 9.28 One‐Way Sensitivity Graph.

Figure 9.29 Two‐Way Sensitivity on Product Features and Market Size with Pro...

Figure 9.30 Two‐Way Sensitivity on Product Features and Market Size and with...

Figure 9.31 Two‐Way Sensitivity on Product Features and Market Size and with...

Figure 9.32 Two‐Way Sensitivity Analysis for Product Features and Market Siz...

Figure 9.33 Geneptin Influence Diagram.

Figure 9.34 Geneptin Drill‐Down ID for Market Share.

Figure 9.35 Geneptin Tornado Diagram.

Figure 9.36 Four Types of Value Functions for Increasing Value.

Figure 9.37 Data Center Location Functional Value Hierarchy.

Figure 9.38 Excel Model Big Picture.

Figure 9.39 Use of Excel Piecewise Linear Function.

Figure 9.40 Data Center Value Components Chart.

Figure 9.41 Data Center Cost vs. Value Plot.

Figure 9.42 Data Center Waterfall Chart.

Figure 9.43 Data Center Sensitivity Analysis for Latency Unnormalized Swing ...

Figure 9.44 Data Center Latency Swing Weight Sensitivity with a Change in th...

Chapter 10

Figure 10.1 Generic Data Fitting Process.

Figure 10.2 Probability Wheel.

Figure 10.3 Expert Assessment Template.

Figure 10.4 RNAS Documentation of Oil Price Assessment.

Chapter 11

Figure 11.1 Step 0 Open Deterministic Model.

Figure 11.2 Step 1 Install the ChanceCalc Monte Carlo Software.

Figure 11.3 Step 2 Assign Probability Distributions.

Figure 11.4 Screen After Step 2.

Figure 11.5 Step 3 Define the Output Cell.

Figure 11.6 Step 4 Add the Output Graphs.

Figure 11.7 Step 5 Add Experiment Table and Index Functions for Decision Var...

Figure 11.8 Step 6 Calculate Outputs and Plot the Results.

Figure 11.9 Step 7 Plot the Cumulative Distribution.

Figure 11.10 Step 8 Plot of the Three Cumulative Distributions.

Figure 11.11 Step 9 Probabilistic Tornado Diagram.

Figure 11.12 Step 10 Create the Excel Dashboard.

Figure 11.13 Example Decision Tree.

Figure 11.14 Decision Tree Solution.

Figure 11.15 Steps to Create the Decision Tree in Figure 11.14.

Figure 11.16 One‐Way Sensitivity Analysis to Low Market.

Figure 11.17 One‐Way Sensitivity Analysis for Year 3 High Market.

Figure 11.18 Two‐Way Sensitivity Analysis.

Figure 11.19 Example Showing the Limitations of Expected Value.

Figure 11.20 Deterministic Dominance Example.

Figure 11.21 Stochastic Dominance Example.

Figure 11.22 Second Deterministic Dominance Example.

Figure 11.23 No Dominance Example.

Figure 11.24 Overlay Distributions to Assess Dominance.

Figure 11.25 Step 1 Create the ID.

Figure 11.26 Step 2 Create the Decision Node.

Figure 11.27 Step 3 Add the Value Node.

Figure 11.28 Step 4a Add the Chance Node.

Figure 11.29 Step 4b Adding the Probabilities for the Chance Node.

Figure 11.30 Step 5 Add the Influence Arcs.

Figure 11.31 Step 6 Add the Outcomes.

Figure 11.32 Expected Value Using an Influence Diagram.

Figure 11.33 Converting an Influence Diagram to a Decision Tree.

Figure 11.34 Decision Tree Created from an Influence Diagram.

Figure 11.35 Conduct the Decision Analysis.

Figure 11.36 Risk Profile and Cumulative Risk Profile Outputs.

Figure 11.37 Precision Tree Screenshot of New Product Problem with Two Uncer...

Figure 11.38 New Product Decision Problem with Perfect Information on Perfor...

Figure 11.39 New Product Decision Problem with Perfect Information on Market...

Figure 11.40 New Product Decision Problem with Perfect Information on Perfor...

Figure 11.41 Influence Diagram Converted into a Decision Tree.

Figure 11.42 Expected Value with Imperfect Information.

Figure 11.43 Assessing Risk Tolerance.

Figure 11.44 EV and CE vs. Size of Deal.

Figure 11.45 Geneptin Flying Bar Chart.

Figure 11.46 Geneptin Waterfall Chart.

Chapter 12

Figure 12.1 Benefit vs. Cost Plot of All Projects.

Figure 12.2 The Football Curve Bounds All Possible Portfolios.

Figure 12.3 RNAS Investment Efficiency Curve.

Figure 12.4 RNAS E&P Production.

Figure 12.5 IT Project Portfolio Example Excel Model Screenshot.

Figure 12.6 IT Portfolio Problem Excel Model Screenshot with Formulas Shown....

Figure 12.7 IT Portfolio Problem Solver Screenshot.

Figure 12.8 Solver Options Screenshot.

Figure 12.9 IT Portfolio Problem Optimal Solution.

Chapter 13

Figure 13.1 Communication.

Figure 13.2 Decision‐Analysis Participants and Communication Paths.

Figure 13.3 Communicating with Senior Leaders.

Figure 13.4 Trade‐off Analytics Hierarchy for an Unmanned Aeronautical Vehic...

Figure 13.5 Chart Used to Tell the Story of the Best Technology.

Figure 13.6 Tar Sands Decision Tree.

Figure 13.7 Data Center Cost vs. Value Plot.

Chapter 14

Figure 14.1 LNG Plant Completion Date Tornado Diagram.

Figure 14.2 Plot of IA Value Versus Life Cycle Phase.

Figure 14.3 Base Practices Causing the IMS Schedule Delay.

Appendix A

Figure A.1 Possibility Tree.

Figure A.2 Possibility (Probability) Tree with Two Distinctions.

Figure A.3 Probability Tree with Two Distinctions.

Figure A.4 Reversing the Order of a Tree.

Figure A.5 Probability Distribution as a Histogram.

Figure A.6 Probability Distribution in Cumulative Form.

Figure A.7 Cumulative Probability Distribution of a Discrete Measure.

Appendix B

Figure B.1 The Decision Conferencing Process.

Appendix C

Figure C.1 Funding Areas and Levels.

Figure C.2 One Possible Portfolio.

Figure C.3 Benefit vs. Cost Plot of One Possible Portfolio.

Figure C.4 Trade Space of Portfolios.

Figure C.5 Selected Portfolio for Cost C, Better Portfolio for Same Cost (X)...

Figure C.6 Curve with Decreasing Slope.

Figure C.7 Curve with Varying Slope.

Figure C.8 Combination of Levels for Curve with Varying Slope.

Figure C.9 Trade Space of Data Center Projects.

Figure C.10 Data for Applications Projects.

Figure C.11 B/C for Applications Projects.

Figure C.12 Efficient Frontier for the Data Center.

Appendix D

Figure D.1 RNAS Decision Hierarchy.

Figure D.2 RNAS Means‐Ends Objectives Hierarchy.

Figure D.3 Roughneck ID Fragment: Decisions.

Figure D.4 RNAS ID Fragment with Decisions, Win‐Scenario Variables, and Valu...

Figure D.5 RNAS ID Fragment with Predecessors of NPV Cash Flow.

Figure D.6 RNAS ID Fragment with Predecessors of Investment/Divestment proce...

Figure D.7 Complete RNAS Influence Diagram.

Figure D.8 RNAS Value Components Chart.

Figure D.9 RNAS Value Components, as Compared to the Growth Strategy.

Figure D.10 RNAS EV Cash Flows.

Figure D.11 RNAS Direct Tornado Diagrams, ENPV $B.

Figure D.12 Direct and Delta Tornado Diagrams for Team Hybrid and Divest Hyb...

Figure D.13 One‐Way Sensitivity Analysis of RNAS Hybrid Strategies to Gas Pr...

Figure D.14 Tar Sands Tornado Diagram.

Figure D.15 Tar Sands Construction Threshold Exploits Optionality.

Figure D.16 Tar Sands Value‐Risk Profiles.

Figure D.17 Tar Sands Decision Tree.

Figure D.18 RNAS S‐Curves.

Figure D.19 RNAS Investment Efficiency Curve.

Guide

Cover

Table of Contents

Title Page

Copyright

Foreword to the 1st Edition

Foreword to the 2nd Edition

Preface

About the Companion Website

Begin Reading

Appendix A: Probability Theory

Appendix B: Decision Conferencing

Appendix C: Resource Allocation with Incremental Benefit/Cost Analysis

Appendix D: Roughneck North American Strategy

Index

End User License Agreement

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Wiley Series inOPERATIONS RESEARCH AND MANAGEMENT SCIENCE

Founding Series EditorJames J. Cochran

Operations Research and Management Science (ORMS) is a broad, interdisciplinary branch of applied mathematics concerned with improving the quality of decisions and processes and is a major component of the global modern movement toward the use of advanced analytics in industry and scientific research. The Wiley Series in Operations Research and Management Science features a broad collection of books that meet the varied needs of researchers, practitioners, policymakers, and students who use or need to improve their use of analytics. Reflecting the wide range of current research within the ORMS community, the Series encompasses application, methodology, and theory and provides coverage of both classical and cutting‐edge ORMS concepts and developments. Written by recognized international experts in the field, this collection is appropriate for students as well as professionals from private and public sectors including industry, government, and nonprofit organization who are interested in ORMS at a technical level. The Series is comprised of three sections: Decision and Risk Analysis; Optimization Models; and Stochastic Models.

Handbook of Decision Analysis

Second Edition

Gregory S. ParnellUniversity of ArkansasFayettevilleAR, USA

Terry A. BresnickBoca RatonFL, USA

Eric R. JohnsonSkillman NJ, USA

Steven N. TaniRetired partner of Strategic Decisions GroupCA, USA

Eric SpeckingUniversity of ArkansasFayettevilleAR, USA

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Library of Congress Cataloging‐in‐Publication Data

Names: Parnell, Gregory S. author | Bresnick, Terry A. author | Johnson,  Eric R. (Writer on Decision making) author | Tani, Steven N. author | Specking,  Eric authorTitle: Handbook of decision analysis / Gregory S. Parnell, University of  Arkansas, Fayetteville, USA, Terry A. Bresnick, Boca Raton, FL, USA,  Eric R. Johnson, Skillman, NJ, USA, Steven N. Tani, Retired partner of  Strategic Decisions Group, CA, USA, Eric Specking,  University of Arkansas, Fayetteville, USA.Description: Second edition. | Hoboken, New Jersey : Wiley, [2025] |  Series: Wiley series in operations research and management science |  Includes index.Identifiers: LCCN 2024039061 (print) | LCCN 2024039062 (ebook) | ISBN  9781394283880 hardback | ISBN 9781394283897 epub | ISBN 9781394283903  adobe pdfSubjects: LCSH: Decision makingClassification: LCC HD30.23 .H3542 2025 (print) | LCC HD30.23 (ebook) |  DDC 658.4/03–dc23/eng/20241023LC record available at https://lccn.loc.gov/2024039061LC ebook record available at https://lccn.loc.gov/2024039062

Cover Design: WileyCover Image: © Vladimir/stock.adobe.com

Foreword to the 1st Edition

This handbook represents a significant advance for decision professionals. Written for practitioners by practitioners who respect the theoretical foundations of decision analysis, it provides a useful map of the tools and capabilities of effective practitioners. I anticipate that this and future editions will become the primary repository of the body of knowledge for practicing decision professionals.

This Handbook Is Timely

The practice of decision analysis (DA) is at a major inflection point. That high‐quality decisions can generate immense value is being demonstrated again and again. Leaders of organizations are increasingly aware of how opportunities are lost by making “satisficing” decisions – that is, decisions that are “good enough.” The benefit‐to‐cost ratio of investing in better decisions is frequently a thousand to one. I know of no better opportunity for value creation anywhere. As Frank Koch,1 president of the Society of Decision Professionals (SDP), has said, “Benefit to cost ratios ... are immense simply because the added cost of doing DA is negligible. We would still be paying the analysts and decision makers without DA; they would simply be talking about different things. The incremental cost of having a better, more relevant conversation is zero, so regardless of the benefit, the ratio is infinite! Even if I throw in the cost of training and learning some software, that's measured in thousands and the benefits are clearly measured in millions.”

Why is this huge opportunity still a secret from most decision makers? It is because we humans are wired to believe that we are making good decisions even when we leave value on the table. We are wired to be satisfied with good enough. We shape our memories with hindsight and rationalization. The burgeoning set of literature from the behavioral decision sciences documents many of our biases and draws attention to the gap between true decision quality (DQ) (see Chapter 5) and our natural decision‐making tendencies.

Our individual cognitive biases are amplified by social behavior, like groupthink. We assume that advocacy decision processes in use by most organizations produce good decisions, yet they are designed to suppress good alternatives. We assume that agreement is the same as DQ, yet we see a lot of agreement around nonsense. It is not uncommon to hear statements like, “I can't believe it – what were we thinking?”

If DQ can create immense additional value in specific decisions, can we develop DQ as an organizational competence? The answer is yes, and Chevron has shown the way. Over the period in which it has implemented a deep and broad adoption of DQ, Chevron has outperformed its peer group of major oil companies in creating shareholder value. While many organizations have pockets of organizational decision quality (ODQ), to my knowledge, Chevron has the broadest and deepest adoption to date. And by “adoption,” I don't just mean better analytics. All the major oil companies have the analytics to address uncertainties and risk. The difference is that the whole Chevron organization seems to be in a passionate and collaborative pursuit of value creation based on quality decisions linked with effective execution. I believe that Chevron's success is the beginning of a big wave of broad adoption of organizational DQ.2

The immense value left behind by our satisficing behaviors represents the biggest opportunity for our business and societal institutions in the coming decades. If we begin to think of these opportunity losses as an available resource, we will want to mine this immense value potential. The courts – led by the Delaware Supreme Court – are raising the bar in their interpretation of a board director's duty of “good faith.” In the coming years, board and top management's best defense is their documented practice of DQ.

Decision Professionals: The Practitioner Perspective

The SDP3 states that the mission of decision professionals is to:

Bring DQ to important and complex decisions.

Provide quality insights and direction through the use of practical tools and robust methodologies.

Promote high professional standards and integrity in all work done by decision professionals.

Advance the profession to the benefit of mankind through helping decision makers.

The role of a decision professional as a practitioner of DA and facilitator of organizational alignment is gaining acceptance. Dozens of organizations have established internal groups of professionals, designed career ladders, and developed specific competency requirements. The recently formed SDP has created a certification process and career ladder that specifies increasing competency levels for practitioners.

While there are important similarities between becoming a successful practitioner and becoming a tenured academic, there are also major differences. The decision professional is motivated by bringing clarity to complex decision situations and creating value potential in support of decision makers. He or she is less interested in the specialization required for peer‐reviewed publication. Instead, the practitioner wants to acquire practical tools and relevant skills that include both analytical and facilitation skills (project management, conflict resolution, and other so‐called “soft skills”).

The ability to address both organizational and analytical complexity (see Figure F.1) are of great importance to the practitioner. As I like to say, “If you can only deal with the analytical complexity, you can get the right answer – but nobody cares. If you can only facilitate in the face of organizational complexity, you can resolve conflicts and gain agreement – but it can be agreement around nonsense.” To bring full value, we need to deliver the combination – agreement around the best answer, the answer that generates the greatest value potential.

Figure F.1 Two Dimensions of Competence.

Individual decision professionals can deliver this if they have the competency in both areas. However, many practitioners are significantly better in one or the other – either strong analytical capabilities or strong social/emotional intelligence and facilitation skills. Therefore, many practitioners find it best to team up with others to deliver the full value of DQ. To make such teaming effective, there must be mutual respect for the other competency and a recognition that value creation from the combination is the goal. It bears repeating: We need to gain alignment around the best answer – the answer that creates the greatest value potential.

As practitioners, we are always approximating and simplifying. We are practical decision engineers and decision facilitators who want robust solutions that are effective in creating a lot of potential value. We are organizational facilitators who are not satisfied unless the best decision is owned by the decision makers and implementers. Incisiveness with tools that produce insight and processes that foster effective engagement is more important to us than another refinement to the axioms of normative decision theory. In my experience, the academic debates at the edges of decision science over the last two decades have contributed surprisingly little to the practice. Seldom is the primary challenge in solving real decision problems a matter of advanced theory.

Our goal should be to make our concepts and methods as simple and accessible as possible. As I am writing this, I am participating in a 2‐week program to teach incoming high school freshmen the basics of DQ and help them apply the concepts to significant school decision projects. I recommend that all decision professionals become engaged with spreading decision skills to youth4 for the simple reason that it will make one a better decision professional. Senior executives and ninth graders have about the same attention span (albeit for different reasons) and want to get to the essence simply and clearly. Even when we employ advanced tools, our results should always be made transparent.

Our Profession

What does it mean to be in a profession? A profession differs from a trade. In providing a professional service, we recognize that the customer cannot fully judge our service and must trust the integrity of the professional to act in the customer's best interest – even when the customer does not wish to hear it. Our customers are the decision makers – the leaders of organizations. We have the responsibility to speak “truth to power.”

We also have the obligation to not “fake it.” Decision professionals must be able to recognize which tools are normative (that is consistent with the norms of decision theory) and which are not but may be useful in practice. We also have to recognize destructive or limited practices. A true decision professional avoids making claims that can be proven to violate the basic norms of decision theory.

As with the medical field, we have to protect our profession from quackery. The profession is beginning to step up to this challenge, taking measures to assure quality and certify competence. This is, of course, a sensitive area in a field that incorporates science, art, and engineering. While I recognize the risks of trying to come to agreement on a definition of decision competence, I support this trend fully and applaud the start that the SDP has made.

The Biggest Challenge

In this nascent profession, our biggest challenge is to gain greater mindshare among decision makers. The fraction of important and complex decisions being made with the support of decision professionals is still very small. We can make faster progress if we unify our brand and naming conventions. I urge all practitioners to use a common language to make more headway with our audiences.

Here are my suggestions:

Let's call ourselves “decision professionals” instead of decision analysts, decision consultants, decision advisors, decision facilitators, and so on.

Let's use the term “decision quality” as the overall name that combines getting to the right answer via DA and gaining organizational alignment via process leadership, decision facilitation, and other soft skills.

Let's refer to DA as the field that provides decision professionals with the analytical power to find the best alternative in situations of uncertainty, dynamics, complex preferences, and complex systems. The use of the term “DA” also means we will be consistent with the norms of decision theory – usually with a single decision‐making body – whose preferences are aligned.

Multiparty decisions – negotiation, collaboration, competition (game theory) – need to become a part of the decision professional's domain of expertise, whether or not these areas are considered a subset of or adjacent to DA.

Decision professionals frequently act as mediators and facilitators with “soft skills” to lead decision processes to reach sound conclusions and to gain alignment and commitment for action. While these skills are not usually considered a part of DA, they are as crucial as model building to the decision professional.

On behalf of the profession, I would like to express my gratitude to Greg Parnell, Steve Tani, Eric Johnson, and Terry Bresnick for creating this handbook. This handbook represents a valuable contribution to the practitioner community. I expect that it will be the first edition of many to come.

CARL SPETZLER

Notes

1

Frank Koch in a written response to the question: What is the ROI of investing in DA based on your experience at Chevron? Frank Koch retired in 2010 after the Chevron team had been awarded the best practice award for

20 Years of DA at Chevron

.

2

See the SDG white paper,

Chevron Overcomes the Biggest Bias of All

(Carl Spetzler, 2011). Available from SDG website,

http://www.sdg.com

.

3

See:

http://www.decisionprofessionals.com

.

4

Check out the Decision Education Foundation at

http://www.decisioneducation.org

.

Foreword to the 2nd Edition

William J. Perry, former Secretary of Defense, holds a bachelor's, master's, and Ph.D., all in mathematics, but does not wear these credentials on his sleeve. At a university gathering after his retirement from the Pentagon, he was asked whether he had created mathematical models on the spot to aid in defense decisions on emergent issues. “No,” he replied, “there was never the time or the data to do that, but because of my training, I think differently about these issues.” Amen. Perhaps the greatest benefit of Decision Analysis (DA) is in terms of changing the way we think.

The first edition of this book was published in 2013 with an excellent foreword by Carl Spetzler, so what has changed since then? Plenty, as it turns out, in the form of technology, which makes DA more important than it was yesterday, and less important than it will be tomorrow. In this regard, I will consider the impact on DA of Ubiquitous Computer Power, Big Data on the Internet, and Artificial Intelligence. From the perspective of someone who has been exposed to DA for seven decades, each of these has changed how I think about the subject.

I got into DA on the ground floor in 1952, when my father, Leonard Jimmie Savage, referenced in Chapter 3 of this book, attempted to explain his axioms of decision theory in terms that a seven‐year‐old (me) would understand. I can't recall how many discussions at the dinner table in the ensuing years involved uncertainty, statistics, and decisions, but I aced my undergraduate courses in probability and statistics. This was despite a very spotty academic record overall, which I shared with my father. I took a single DA course in graduate school, and then in the mid‐1990s, as an adjunct professor at Stanford, had the great fortune to sit in on Ron Howard's course on DA. Like others I know who took this course, it was a turning point in our lives. Ron had the ability to state simple obvious truths that, oddly, I had never thought about before but would never forget thereafter. I continue to be involved in decision‐making in the face of uncertainty, through teaching, consulting, and the development of standards and software in this area.

Ubiquitous Computer Power

I will start with a quote from my father's 1954 book, The Foundations of Statistics. He describes a theory of rational decision‐making based on the principle that people will correctly assessuncertainties and make rational decisions to maximize their expected gain. But then he says that from a practical point of view:

… the task implied in making such a decision is not even remotely resembled by human possibility. It is even utterly beyond our power to plan a picnic or to play a game of chess in accordance with the principle …

This may be jarring to today's DA practitioner, but at the time he wrote this there were on the order of 100 computers in the United States, each in its own large building. Had he lived to see a computer declared the world chess champion in 1997, no doubt based on the principles of DA, I am sure he would have modified this statement. In fact, in 1970 he recognized that the cost of computation would become a factor in future systems of statistical thought. In the decade after his death in 1971, computational statistics extending Julian Simon's approach to resampled data, and Brad Efron's Bootstrap became cornerstones of this approach. Not coincidentally, DA has been moving away from decision trees and toward Monte Carlo Simulation, although I believe both are important and should be integrated when possible. As further evidence of ubiquitous computer power, today, native Excel, with its roughly one billion users, can perform tens of thousands of Monte Carlo trials before the user's finger leaves the enter key. Chapter 11 of this book takes advantage of these developments to bring interactive simulation to students of DA in a form they can easily take with them to the workplace to help their organizations make rational decisions to maximize their expected gain.

Big Data on the Internet

Big data on the Internet has had a profound effect on helping people to correctly assessuncertainties.

Prediction Markets

My father's book took the position that probabilities were subjective and could ultimately be elicited by gambles that people were willing to make around uncertain events. Such wagers on sporting outcomes may date back to the dawn of civilization. Today, prediction markets on the Internet extend this concept beyond the realm of sports to events in areas as diverse as politics, economics, medicine, science, and corporate decision‐making. They bring the wisdom of crowds to assessing uncertainties in a way that my father would have applauded but could not have dreamt of.

Smartphones

Smartphones have revolutionized the way we gather and consume information. For example, cities used to spend many thousands of dollars hiring people to drive over their street network and report potholes. Today, we may harness the gravitometers in phones in thousands of cars, which when coupled to their GPS coordinates can accomplish in hours what used to take days. And as to planning picnics? I just discovered a phone app called Next Picnic, which helps you discover picnic and barbecue spots in nature, complete with real‐time weather updates to plan your activities accordingly. It also allows users to rate and review locations, enhancing the planning experience. This app along with online weather information could certainly inform a decision analytic picnic decision.

Communicating Uncertainty with Stochastic Data

Borrowing from techniques developed in the 1980s in the financial engineering and insurance industries, data containing the results of Monte Carlo simulations may be stored on the web and shared between simulations to aggregate results in portfolio decisions as discussed in Chapter 12. Furthermore, recent mathematical techniques have resulted in a massive compression of such data, greatly increasing its flexibility and applicability.

Artificial Intelligence

This represents the future of DA, to which the contents of this book nonetheless apply. The only prediction we can make about AI in 2025 is that its evolution will be unpredictable. However, I believe that AI is already providing important opportunities for DA. I have listed a few that come to mind.

Extracting Probabilities from AI

General statistical knowledge

We are already at the stage where you can ask a Chatbot to provide parameters of probability distributions on subjects as diverse as how many bottles of wine to purchase for a party, to the distribution of remaining years of life for a person of specified age and gender. In some cases, the AI may even run a Monte Carlo simulation without being asked, to generate a probability distribution. How accurate is it? Who knows, but it is getting smarter at an alarming rate, and DA professionals must learn to master and validate this new source of probabilities.

The Residual Errors of Machine Learning

Machine learning dates back to the famous mathematician Gauss, who used least squares regression to make predictions about astronomical data. And like the machine learning of today, it involved creating a predictive model with residual errors, which estimate the accuracy of the model itself. DA professionals need not become experts in machine learning themselves. However, they should view it as a potentially rich source of probabilities.

The Value of AI

An extremely important concept in DA is the value of information. That is, what would it be worth economically to learn today whether or not event E will occur tomorrow. If one learns definitively this is known as the value of perfect information. But if, like a weather forecast, it is sometimes wrong, it is known as imperfect information. Now think of AI as a generalization of a weather forecast. It will be important for the DA professional to learn to use AI and to evaluate the accuracy of its predictions in a rapidly evolving environment.

Autonomous Systems – Meta Decision Analysis

We are well into the era of autonomous systems. That is, we are making design decisions about systems that will make their own decisions! I will call this Meta Decision Analysis until someone comes up with a better term. Here are some examples.

Autonomous Cars

Autonomous cars must make decisions such as, “Do I run over the three kids waiting for the school bus or do I slam my own driver into a tree to avoid hitting the kids?” I hope decision analysts are consulted in the design of such systems.

Autonomous Military Systems

Autonomous military systems such as swarms of drones will inevitably not always be under the direct control of a human. In fact, one can imagine a ratio of autonomous systems to humans of 10 to 1 or perhaps even 100 to 1. Good decision analysis will be vital to designing the autonomous decision capability of such systems.

Far from making DA obsolete, current technological advances are making it mandatory. And the material in this book is as relevant to the future of DA as it was to the past.

Preface

Our Decision Analysis Handbook 2nd Edition is written for the decision professional. Decision analysts work in many industries and government agencies; many work in oil and gas firms, pharmaceutical firms, and military/intelligence agencies. The target audience is the decision analysis practitioner who wants to increase the breadth and depth of his or her technical (concepts and mathematics) and soft skills (personal and interpersonal) required for success in our field. We assume the reader has a technical (engineering, science, mathematics, or operations research) or a business degree; a course in probability and statistics (Appendix A provide a probability review); and, perhaps, some introduction to single or multiple objective decision analysis in a college course or a professional short course. The book is not designed to introduce new decision analysis mathematics but rather to make the most common mathematics and best practices available to the practitioner.

The handbook is designed as a text for an undergraduate or graduate course in decision analysis, as a supplemental reading for professional decision analysis training courses, or as a reference for beginning and experienced practitioners. The 2nd edition now has a New Product Development NPV case study and a Data Center location case study including screen shots of Excel and Excel Addin Software solutions. The book should be useful to both domestic and international practitioners.

Another new feature of the 2nd Edition is the emphasis on the use of big data, analytics and artificial intelligence in decision analysis and the use of decision framing tools in data analytic decision problems.

Our handbook describes the philosophy, technical concepts, mathematics, and art of decision analysis for the decision professional. The handbook includes chapters on the following topics: decision making challenges; mathematical foundations of decision analysis; decision analysis soft skills, selecting the decision making process for interacting with decision makers and stakeholders; framing the decision; crafting decision objectives; designing creative alternatives to create value; performing deterministic modeling and analysis of alternatives; assessing uncertainty; performing probabilistic modeling and analysis; portfolio decision analysis; communicating with senior decision makers; and implementing decisions.

Figure P.1 provides the organizational structure to the book. Chapter 1 provides an introduction to decision analysis. Chapters 2–4 provide the foundational knowledge required for decision analysis success. Chapters 5–14 provide the decision analysis best practices to create value as sequential, iterative steps. However, the order of the steps should be tailored to the application and some steps may not apply. For example, if the decision is a choice of the best alternative, the portfolio decision analysis chapter would not apply. Also, some steps can be combined. For example, the decision framing and crafting of the decision objectives may be done at the same time. Chapter 15 provides a summary of the major themes of the book.

Figure P.1 Chapter organization of the Decision Analysis Handbook.

The book also includes key insights from decision analysis applications and behavioral decision analysis research. The handbook references decision analysis textbooks, technical books, and research papers for detailed mathematical proofs, advanced topics, and further professional reading.

The handbook has five unique features.

The book provides a balanced presentation of technical skills (decision analysis concepts, mathematics, and modeling) and of soft skills (strategic thinking. leading teams, managing teams, researching, interviewing individuals, facilitating groups, and communicating).

The book integrates the techniques of single and multiple objective decision analysis instead of presenting them in separate sections of the book.

Chapter 3

provides our framework.

The book uses four substantive illustrative examples (TechnoMagic New Product Development, Data Center Location, Geneption Personalized Medicine, and Roughneck North American Strategy (in Appendix D)) to illustrate the key decision analysis concepts and techniques; show the diversity of applications, and demonstrate how the techniques are tailored to different decision problems.

The book presents multiple qualitative and quantitative techniques for each key decision analysis task as opposed to presenting one technique for all problems. After describing the techniques, we discuss their advantages and disadvantages.

Wiley provides a website (

www.wiley.com/go/Decision_Analysis_2e

) for the handbook. This website will updated information on the book, Power Point slides for instructors, and the Excel files used in the textbook for instructors and students.

As the co‐authors, we became decision analysts and strive to be decision professionals because we believe in the power of decision analysis to create value for organizations and enterprises. The art and science of decision analysis has changed our professional and personal decision making.

Writing the handbook has been a great opportunity for us to reflect on what we have learned and to describe the best practices that we use. In addition to our mentors and colleagues, we have also learned from each other in the process of writing (and rewriting) this book! We look forward to hearing your comments on the book and we hope that the material helps your development as a decision professional.

Gregory S. Parnell

Terry A. Bresnick

Eric R. Johnson

Steven N. Tani

Eric Specking

About the Companion Website

This book is accompanied by a companion website:

www.wiley.com/go/Decision_Analysis_2e 

This website includes:

Students

New Product Decision Deterministic Model Template

Data Center Decision Deterministic Model Template

Data Center IT Project Portfolio Template

RNAS Excel Model

Instructors

Syllabus for 8W course

Schedule for 8W course

PowerPoint Presentations for

Chapters 1

to

14

and Appendix A

Individual and Group Homework Assignments

Group Project Assignment

Group Project Team Charter

Uncertainty Pretests 1 and 2

New Product Decision Deterministic Model Template

New Product Deterministic Model Solution

New Product Decision Probabilistic Model Template

New Product Decision Probabilistic Model Solution

Data Center Decision Deterministic Model Template

Data Center Deterministic Model Solution

Monte Carlo Models and Solutions for the Text Examples

Decision Tree Models and Solutions for the Text Examples

Influence Diagrams Models and Solutions for the Text Examples

Data Center IT Project Portfolio Template

Data Center IT Project Portfolio Solution

PDF Presentations for

Chapters 1

to

14

and Appendix A

1Introduction to Decision Analysis and Analytics

Nothing is more difficult, and therefore more precious, than to be able to decide.

—Napoleon, Maxims, 1804

In God we trust. All others must bring data.

—W. Edwards Deming, statistician, professor, author, lecturer, and consultant

CHAPTER MENU

1.1 Introduction

1.2 Decision Analysis is a Social‐Technical Process

1.3 Decision Analysis Applications

1.3.1 Oil and Gas Decision Analysis Success Story – Chevron

1.3.2 Pharmaceutical Decision Analysis Success Story – SmithKline Beecham

1.3.3 Military and Intelligence Decision Analysis Success Stories

1.4 Decision Analysis Practitioners and Professionals

1.4.1 Education and Training

1.4.2 Decision Analysis Professional Organizations

1.4.3 Problem Domain Professional Societies

1.4.4 Professional Service

1.5 Handbook Overview and Illustrative Examples

1.5.1 TechnoMagic New Product Launch

1.5.2 Geneptin Personalized Medicine for Breast Cancer

1.5.3 Data Center Location and IT Portfolio

1.5.4 Roughneck North American Strategy (RNAS)

1.6 Summary

Key Terms

References

1.1 Introduction

The consequences of our decisions directly affect our professional and personal lives. As Napoleon noted in our opening quote, decisions can be difficult, and making good decisions can be very valuable. Having the right data and information at the right time with the right process enables decision‐making. Our focus is on professional decisions, but the same principles apply to our personal decisions.

We begin by defining a decision. Howard defined a decision as an irrevocable allocation of resources (Howard, 1988). Consider the contracting process used by many companies and organizations. The company does not make a decision to buy a product or service when they begin thinking about the procurement. They make the decision when they sign a legally binding contract, which requires them to provide resources (typically dollars) to the supplier of the product or service. Can they change their mind? Absolutely, but they may have to pay contract cancellation fees.

A decision is an irrevocable allocation of resources.

Decisions are made by decision‐makers (DMs) vested with the authority and responsibility to make decisions for an organization or enterprise. Many decisions involve stakeholders (SHs) who are individuals and organizations that could be affected by the future consequences of the decision. Some decisions are easy because few SHs are involved, the values are clear, good alternatives are readily identified, and there are few uncertainties. However, some difficult decisions involve many SHs with potentially conflicting objectives, complex alternatives, large amounts of data, significant uncertainties, and large consequences. The discipline of decision analysis, the focus of this handbook, has been developed to help DMs with these complex decisions. This second edition builds on the first edition by incorporating data analytics information into decision analysis.

There are many definitions of decision analysis. Howard, who coined the term “decision analysis”, defined decision analysis as “a body of knowledge and professional practice for the logical illumination of decision problems” (Howard, 1966). In the first book on decision analysis, Raiffa defined decision analysis as an approach that “prescribes how an individual faced with a problem of choice under uncertainty should go about choosing a course of action that is consistent with personal basic judgments and preferences” (Raiffa, 1968). Keeney provided an intuitive and technical definition. Keeney's intuitive definition was “a formalization of common sense for decision problems that are too complex for informal use of common sense” (Keeney, 1982). His technical definition was “a philosophy, articulated by a set of logical axioms, and a methodology and collection of systematic procedures, based on those axioms, for responsibly analyzing the complexities inherent in decision problems.” Phillips emphasized that decision analysis is a social‐technical process to provide insights to DMs in organizations (Phillips, et al., 1990) and (Phillips, 2005