Handbook of Decision Analysis

Name: Handbook of Decision Analysis
Brand: Wiley
Price: 104.99 EUR
Availability: OnlineOnly

Gregory S. Parnell Terry A. Bresnick Eric R. Johnson Steven N. Tani Eric Specking(Autor*in)

Wiley (Verlag)

2. Auflage

Erschienen am 22. April 2025

401 Seiten

E-Book

PDF mit Adobe-DRM

Systemvoraussetzungen

978-1-394-28390-3 (ISBN)

104,99 €inkl. 7% MwSt.

Systemvoraussetzungen

für PDF mit Adobe-DRM

E-Book Einzellizenz

Als Download verfügbar

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.

Weitere Details

Weitere Ausgaben

Personen

Inhalt

Cover
Title Page
Copyright
Contents
Foreword to the 1st Edition
Foreword to the 2nd Edition
Preface
About the Companion Website
Chapter 1 Introduction to Decision Analysis and Analytics
1.1 Introduction
1.2 Decision Analysis is a Social-Technical Process
1.3 Decision Analysis Applications
1.3.1 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
References
Chapter 2 Decision-Making Challenges
2.1 Introduction
2.2 Human Decision-Making
2.3 Decision-Making Challenges
2.4 Organizational Decision Processes
2.4.1 Culture
2.4.2 Impact of Stakeholders
2.4.3 Decision Level (Strategic, Tactical, Operational)
2.5 Credible Problem Domain Knowledge
2.5.1 Dispersion of Knowledge
2.5.2 Technical Knowledge - Essential for Credibility
2.5.3 Business Knowledge - Essential for Success
2.5.4 Role of Experts
2.5.5 Limitations of Experts
2.6 Behavioral Decision-Analysis Insights
2.6.1 Decision Traps and Barriers
2.6.2 Cognitive Biases
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
2.8.1 TechnoMagic New Product Launch
2.8.2 Geneptin Personalized Medicine
2.8.3 Data Center Decision Problem
2.9 Summary
Key Terms
References
Chapter 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
3.6.1 Some DA Terminology
3.6.2 Taxonomy Division-Single or Multiple Objectives
3.6.2.1 Single-Objective Decision Analysis
3.6.2.2 Multiple-Objective Decision Analysis
3.6.3 Addressing Value Trade-Offs and Risk Preference Separately or Together?
3.6.4 Nonmonetary or Monetary Value Metric?
3.6.5 Degree of Simplicity in Multidimensional Value Function
3.7 Value-Focused Thinking
3.7.1 Four Major VFT Ideas
3.7.2 The Benefits of VFT
3.8 Summary
Key Terms
Acknowledgments
References
Chapter 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
4.6.1 Before the Interview
4.6.2 Schedule/Reschedule the Interview
4.6.3 During the Interview
4.6.4 After the Interview
4.7 Conducting Surveys
4.7.1 Preparing an Effective Survey: Determine the Goals, Survey Respondents, and Method for Collecting Survey Data
4.7.2 Executing a Survey Instrument: Developing the Survey Questions, Testing, and Distributing the Survey
4.8 Facilitating Groups
4.8.1 Facilitation Basics
4.8.2 Group Processes
4.8.2.1 Stages of Group Development
4.8.2.2 Planning
4.8.2.3 Pulsing
4.8.2.4 Pacing
4.8.3 Focus Groups
4.8.3.1 Preparing for the Focus Group Session
4.8.3.2 Executing the Focus Group Session
4.9 Aggregating across Experts
4.10 Communicating Analysis Insights
4.11 Summary
References
Chapter 5 Use the Appropriate Decision Process
5.1 Introduction
5.2 What Is a Good Decision?
5.2.1 Decision Quality
5.2.2 The Six Elements of Decision Quality
5.2.3 Intuitive vs. Deliberative Decision-Making
5.2.4 Artificial Intelligence-Driven Decision-Making
5.3 Selecting the Appropriate Decision Process
5.3.1 Tailoring the Decision Process to the Decision
5.3.1.1 How Urgent Is the decision?
5.3.1.2 How Important Is the Decision?
5.3.1.3 Why Is This Decision Difficult to Make?
5.3.2 Two Best Practice Decision Processes
5.3.2.1 Dialogue Decision Process
5.3.2.2 Decision Conferencing
5.3.3 Two Flawed Decision Processes
5.3.3.1 Strictly Analytical Decision Processes
5.3.3.2 Advocacy Decision Processes
5.4 Decision Processes in Illustrative Examples
5.4.1 TechnoMagic New Product Launch
5.4.2 Geneptin Personalized Medicine
5.4.3 Data Center Location Decision
5.5 Organizational Decision Quality
5.6 Decision-Maker's Bill of Rights
5.7 Summary
References
Chapter 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
6.4.1 Vision Statement
6.4.2 Issue Raising
6.4.3 Categorization of Issues
6.4.4 Decision Hierarchy
6.4.5 Values and Trade-offs
6.4.6 Initial Influence Diagram
6.4.7 Decision Schedule and Logistics
6.5 Using an Influence Diagram for Decision Framing
6.5.1 Introduction to Influence Diagrams
6.5.2 Influence Diagram Elements
6.5.3 Influence Diagram Rules
6.6 Framing the Decision Opportunities for the Illustrative Examples
6.6.1 TechnoMagic New Product Launch
6.6.2 Geneptin Personalized Medicine
6.6.3 Data Center Decision
6.7 Using Decision-Analysis Techniques to Frame Analytics Projects
6.8 Summary
Key Terms
References
Chapter 7 Craft the Decision Objectives and Value Measures
7.1 Introduction
7.2 Shareholder and Stakeholder Value
7.2.1 Private Company Example
7.2.2 Government Agency Example
7.3 Challenges in Identifying Objectives
7.4 Identifying the Decision Objectives
7.4.1 Questions to Help Identify Decision Objectives
7.4.2 How to Get Answers to the Questions
7.5 The Financial or Cost Objective
7.5.1 Financial Objectives for Private Companies
7.5.2 Cost Objective for Public Organizations
7.6 Developing Value Measures
7.7 Structuring Multiple Objectives
7.7.1 Value Hierarchies
7.7.2 Techniques for Developing Value Hierarchies
7.7.3 Value Hierarchy Best Practices
7.7.4 Cautions about Cost, Risk and -ilities Objectives
7.8 Illustrative Examples
7.8.1 TechnoMagic New Product Decision
7.8.2 Geneptin
7.8.3 Data Center Location
7.9 Summary
References
Chapter 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
8.7.1 TechnoMagic New Product Launch
8.7.2 Geneptin Personalized Medicine
8.7.3 Data Center Location
8.8 Summary
References
Chapter 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
9.3.1 Guidelines for Building a Spreadsheet Decision Model
9.3.2 Scenario Analysis
9.4 Deterministic Modeling with Net Present Value
9.4.1 Net Present Value Calculation
9.4.1.1 Explanation of NPV
9.4.1.2 Simple NPV Example
9.5 Two Illustrative NPV Examples
9.5.1 TechnoMagic New Product Launch
9.5.1.1 Control Panel Worksheet
9.5.1.2 Calculations Worksheet
9.5.1.3 Determining the Best Decisions Using Excel's What If Analysis
9.5.1.4 Additional Sensitivity Analysis
9.5.2 Geneptin NPV Example
9.6 Deterministic Modeling Using Multiple-Objective Decision Analysis
9.6.1 The Additive Value Function
9.6.2 Single-Dimensional Value Functions
9.6.3 Swing Weights
9.6.4 Swing Weight Matrix
9.6.4.1 Consistency Rules
9.6.4.2 Assessing Unnormalized Swing Weights
9.6.4.3 Calculating Normalized Swing Weights
9.6.4.4 Benefits of the Swing Weight Matrix
9.6.5 Scoring the Alternatives
9.6.6 Deterministic Analysis
9.7 Illustrative MODA Problem - Data Center Location
9.7.1 Additive Value Model
9.7.2 Decision Analysis Software
9.7.3 Value Functions
9.7.4 Swing Weight Matrix
9.7.5 Scoring the Alternatives
9.7.6 Implementing the MODA Model in Excel
9.7.6.1 Single-Dimensional Value Calculations
9.7.6.2 Normalized Swing Weight Calculations
9.7.6.3 Alternative Value Calculations
9.7.6.4 Value Components
9.7.6.5 Value Gaps
9.7.6.6 Data Center Life Cycle Costs (LCCs)
9.7.6.7 Value vs. Cost
9.7.6.8 Waterfall Char
9.7.6.9 Sensitivity Analysis
9.7.6.10 Value-Focused Thinking
9.8 Summary
Key Terms
References
Chapter 10 Quantify Uncertainty{\textit {Uncertainty}}
10.1 Introduction
10.2 Use the Influence Diagram to Develop Probability Distributions
10.3 Probability Assessment with Data
10.3.1 General Process
10.4 Elicit and Document Subject Matter Expert Assessments
10.4.1 Heuristics and Biases
10.4.2 Reference Events
10.4.3 Assessment Protocol
10.4.4 Assessing a Continuous Distribution
10.4.5 The Reluctant Expert
10.4.6 Making Assumptions to Inform Probabilistic Modeling
10.5 Box Assessment Protocols with Artificial Intelligence Tools
10.6 Illustrative Examples
10.7 Summary
Endnotes
Key Terms
References
Chapter 11 Perform Probabilistic Analysis and Identify Insights
11.1 Introduction
11.2 Exploration of Uncertainty: Simulation, Decision Trees, and Influence Diagrams
11.2.1 Software for Simulation, Decision Trees, and Influence Diagrams
11.2.2 Simulation
11.2.3 TechnoMagic New Product Launch Monte Carlo Simulation
11.2.4 Decision Trees
11.2.4.1 Introduction
11.2.4.2 Elements of a Decision Tree
11.2.4.3 Solving a Decision Tree
11.2.4.4 Product Launch Decision
11.2.4.5 How to Solve a Decision Tree
11.2.4.6 New Product Decision-Tree Solution
11.2.4.7 One-Way Sensitivity Analysis
11.2.4.8 Two-Way Sensitivity Analysis
11.2.4.9 Limitations of Expected Value and Flaw of Averages
11.2.4.10 Dominance
11.2.5 Influence Diagrams
11.2.5.1 Solving the Product Launch Decision with an Influence Diagram
11.2.5.2 Converting the Influence Diagram to a Decision Tree
11.2.5.3 Risk Profiles
11.2.5.4 Comparison of Decision Trees and Influence Diagrams
11.2.6 Choosing Between Monte Carlo Simulation and Decision Trees
11.2.6.1 Downstream Decisions
11.2.6.2 Number of Uncertainties
11.2.6.3 Anomalies in the Value Function
11.3 Value of Information and Value of Control
11.3.1 Introduction
11.3.2 Value of Information
11.3.3 New Product Problem
11.3.4 Perfect Information
11.3.5 Expected Value of Control
11.3.6 Imperfect Information
11.3.7 Comparison of Information and Control
11.4 Risk Attitude
11.4.1 Delta Property
11.4.2 Exponential Utility
11.4.3 Assessing Risk Tolerance
11.4.4 Calculating Certain Equivalents
11.4.5 Evaluating "Small" Risks
11.4.6 Going Beyond the Delta Property
11.5 Illustrative Examples
11.5.1 Geneptin Example
11.5.2 Data Center
11.6 Summary
Key Terms
References
Chapter 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
12.4.1 Characteristics of Portfolio Optimization
12.4.2 Greedy Algorithm Using Profitability Index and the Efficient Frontier
12.4.3 Application to Roughneck North American Strategy Portfolio
12.4.4 Portfolio Risk Management
12.4.5 Trading off Financial Goals with other Strategic Goals
12.5 Multiobjective Portfolio Analysis with Resource Constraints
12.5.1 IT Project Portfolio Problem
12.5.2 Constraint Precision
12.5.3 Integer Optimality
12.6 Summary
References
Chapter 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
13.4.1 Tell the Decision-Maker the Key Insights and Not the Details
13.4.2 Communicating Quantitative Information
13.4.3 Finding and Telling the Story
13.4.4 Best Practices for Presenting Decision-Analysis Results
13.4.5 Best Practices for Written Decision-Analysis Results
13.5 Communicating Insights in the Illustrative Examples
13.5.1 Roughneck North America Strategy
13.5.2 Geneptin
13.5.3 Data Center Location
13.6 Summary
References
Chapter 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
14.4.1 Using the Decision Model for Decision Implementation
14.4.2 Using Decision-Analysis Models to Support Decision Implementation
14.4.2.1 Example 1: Gas Plant Implementation
14.4.2.2 Example 2: Information Assurance Program Progress
14.4.3 Using Decision Analysis to Assess Strategy Implementation
14.4.3.1 Example
14.5 Illustrative Examples
14.5.1 Data Center
14.5.2 RNAS
14.6 Summary
References
Chapter 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
15.3.1 Single Objective Value
15.3.2 Multiple Objective Value
15.3.3 It Is Important to Distinguish Potential Value and Implemented Value
15.4 Decision Analysis Is a Sociotechnical Process
15.4.1 Social
15.4.2 Technical
15.5 Decision Analysts Need Decision-Making Knowledge and Soft Skills
15.5.1 Decision Analysts Need to Understand Decision-Making Challenges
15.5.2 Decision Analysts Must Develop Their Soft Skills
15.6 The Decision-Analysis Process Must Be Tailored to the Decision and the Organization
15.6.1 Decision Quality
15.6.2 Decision Processes
15.6.2.1 Decision Conferencing
15.6.3 Dialogue Decision Process
15.7 Decision Analysis Enables Data-Driven Decision-Making
15.8 Decision Analysis Offers Powerful Analytic Tools to Support Decision-Making
15.8.1 Decision Framing
15.8.2 Identifying Objectives and Value Measures
15.8.3 Developing Creative Alternatives
15.8.4 Building Decision Models
15.8.5 Performing Deterministic Analysis
15.8.6 Identifying Uncertainties
15.8.7 Performing Probabilistic Analysis
15.8.8 Performing Portfolio Resource Allocation
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.11.1 Summary Statistics for a Probability Distribution
A.12 Combining Uncertain Quantities
References
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
References
Appendix C Resource Allocation with Incremental Benefit/Cost Analysis
C.1 Multiple Objective Portfolio Analysis with Resource Constraints
C.1.1 Characteristics of Incremental Benefit/Cost Portfolio Analysis
C.1.2 Algorithm for Incremental Benefit/Cost Portfolio Analysis
C.1.2.1 Identify the Objective
C.1.2.2 Generate Options
C.1.2.3 Assess Costs
C.1.2.4 Assess Benefits
C.1.2.5 Specify Constraints
C.1.2.6 Allocate Resources
C.1.2.7 Perform Sensitivity Analysis
C.1.3 Application to the Data Center Portfolio
C.1.4 Comparison with Portfolio Optimization
C.1.5 Strengths and Weaknesses of Incremental Benefit/Cost Portfolio Analysis
C.2 Summary
Key Terms
References
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.8.1 Selectors
D.8.2 Inputs Sheet
D.8.3 Strategy Table Sheet
D.8.4 Calculations Sheets
D.9 Probabilistic Analysis
D.10 Real Options
D.11 Portfolio Resource Allocation
Reference
Index
EULA

Systemvoraussetzungen

Als PDF speichern Als Link merken