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Introduction to Decision Analysis

A Practitioner's Guide to Improving Decision Quality

Second Edition

David C. Skinner
from the PREFACE:
This book will introduce you to a new way of thinking.  When making difficult decisions in a complex and uncertain environment, it is essential to identify important characteristics and uncertainties affecting the problem so you can develop a clear and compelling course of action.  This book shows you how to correctly structure a problem, understand the inputs and outputs of evaluation, and gain insight into the appropriate course of action.

The idea for this book came when I was teaching a group of executives in 1992.  They asked what book I would recommend for them to use as a reference on the decision analysis process.  While there are many good books on decision analysis, most focus on theoretical and mathematical aspects of decision analysis -- not decision consulting.  I decided to write a book that would incorporate enough of the theoretical and mathematical aspects to provide a good understanding of decision analysis -- but from a consultant's viewpoint.

My goal in writing this book was to develop a format that could be used for corporate training as well as for final year undergraduates or beginning graduate students in management.  We present the most recent decision analysis techniques at a level understandable by anyone with a background in simple mathematics.

I believe this is till the only book written by a practitioner for a practitioner.  What this means to you is that the book presents and discusses concepts using real world business cases.  The book will take you step by step through the decision analysis process.  I have filled the book with helpful tips and tools, and I have included stories of what has worked well and what has not.

From rear cover:

Proven techniques and solid experience are the foundation for this classic text... for the manager and the practitioner.

As decision makers, we often face complex, challenging, uncertain, and often ambiguous choices.  When making these choices, we can follow our intuition or "gut" instincts, or we can pursue a more analytical approach.  Decision analysis provides a process whereby all parties affected by the decision can be involved and participate in building a win-win solution.

"This book will revolutionize your thinking."  -- JT Lewellen, VP Marketing, Turn-Key Specialists, Inc.
Table of Contents
Chapter 1 Introduction
The traditional business decision process.
How does this book differ from other books on decision analysis?
Key terms and concepts.
What is decision analysis?
Why use decision analysis?
The origins of decision analysis.
Applying decision analysis.
Where are we going from here?
Case for analysis.
Chapter 2  Decision making in a complex world.
Why are decisions difficult?
Consequences, uncertainty, and ambiguity.
A scalable process -- uncertainty and ambiguity
Real world decisions
The role of decision analysis.
Case for analysis.
Chapter 3  Uncertainty and making choices.
Decisions and uncertainty.
Measures of merit.
Time value of money.
Dealing with risk.
The certain equivalent.
Principals of evaluations.
Using distinctions.
Defining possibilities.
Case for analysis.
Chapter 4  Making compelling decisions.
The decision elements.
Why we have difficulty achieving high-quality decisions.
How do you achieve decision quality?
The ten principles of good decision-making.
How do you measure good decision quality?
Case for analysis.
Chapter 5  The scalable decision process
Is SDP different than traditional decision analysis?
Structuring phase.
Evaluation phase.
Agreement phase.
Case for analysis.
Chapter 6  Creating a shared understanding of the problem.
Framing the problem.
The participants in the process.
Developing an appropriate time frame.
Creating alternatives.
Case for analysis.
Chapter 7  Developing a decision model
Building influence diagrams
Decision trees.
Computer modeling programs.
Case for analysis.
Chapter 8  An introduction to probability
What is probability.
Probability basics.
Venn diagrams.
States of information.
Probability trees
Reversing the tree.
Using and understanding distributions.
Case for analysis.
Chapter 9  Using simulation to solve decision problems
What is a Monte Carlo simulation?
Why use Monte Carlo simulation?
Use random numbers to simulate reality.
Using the results of a Monte Carlo.
Commercial software.
The role of Monte Carlo.
Case for analysis.
Chapter 10  Using uncertain information and judgment.
Using limited information.
Gathering information.
Assessing information.
Discretizing the information.
Chapter 11 Gaining insight through evaluation.
Deterministic sensitivity analysis.
Probabilistic evaluation.
Value of information.
Applying an appropriate risk attitude
Axioms of rational thought.
Chapter 12  Getting to agreement
Agreement and implementation.
Developing a decision quality package.
Preparing the organization.
Case for analysis.
Chapter 13  A thirty-minute guide to better decisions
Company background.
The process.
Pre-project planning.
Kickoff meeting.
Structuring phase.
Evaluation phase.
Evaluation meeting.
Decide on a course of action.
Allocate the appropriate resources.
Integrate the course of action.
Chapter 14  Using SDP on large projects
Understanding the scalable decision process (SDP).
Identification of problem/opportunity.
Asses the business situation.
Generate creative alternatives.
Model the opportunity.
Discover what is important.
Quantify risk and return.
Value of new information.
Decide on course of action.
Allocate appropriate resources.
Implement course of action (integrate the solution).
16 Implementing the decision analysis process
Implementing decision analysis.
What is right for your organization?
Implementation issues.
Real-world problems.
Implications and reactions.
Summary and interpretation.
Appendix A:  How 2 Guides
Follow the scalable decision process.
Elicit issues.
Build influence diagrams.
Develop strategy tables.
Create a decision hierarchy.
Assess data ranges.
Build decision trees.
Appendix B:  Decision response inventory exercise.
Appendix C:  Discount rate table
Appendix D:  Decision quality radar chart.
Appendix E:  Probability encoding chart.