Making Hard Decisions with Decision Tools Suite

ISBN-10: 0495015083
ISBN-13: 9780495015086
Edition: 2001 (Revised)
List price: $303.95
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Description: MAKING HARD DECISIONS WITH DECISIONTOOLS is a special version of Bob Clemen's best-selling text, MAKING HARD DECISIONS. This straight-forward book teaches the fundamental ideas of decision analysis, without an overly technical explanation of the  More...

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Book details

List price: $303.95
Copyright year: 2001
Publisher: Brooks/Cole
Publication date: 12/8/2004
Binding: Hardcover
Pages: 752
Size: 7.75" wide x 9.75" long x 2.25" tall
Weight: 3.344
Language: English

MAKING HARD DECISIONS WITH DECISIONTOOLS is a special version of Bob Clemen's best-selling text, MAKING HARD DECISIONS. This straight-forward book teaches the fundamental ideas of decision analysis, without an overly technical explanation of the mathematics used in management science. This new version incorporates and implements the powerful DecisionTools by Palisade Corporation, the world's leading toolkit for risk and decision analysis. At the end of each chapter, topics are illustrated with step-by-step instructions for DecisionTools. This new version makes the text more useful and relevant to students to business and engineering.

Robert T. Clemen is Professor at the Fuqua School of Business at Duke University. He has been an active contributor to the field of decision analysis since earning his Ph.D. at Indiana University in 1984 and is an authority in the use of expert information in decision and risk analysis. He has interests in applying decision analysis in the areas of energy use and corporate sustainabiligy. Aside from his research interests, Bob has a strong focus on making decision analysis understandable to all students at all levels, ranging from high school students to executives.

Terence Reilly is an associate Professor of Mathematics at Babson College. His interests include developing innovative curriculum for MBA programs, particularly distance programs, along with simulation and decision analysis. His research interests are in developing sensitivity analysis techniques and fitting distribution curves to univariate and multivariate uncertainties. Recently, he has developed courses in financial methods using simulation and decision analysis.

Preface
Introduction To Decision Analysis
Why Are Decisions Hard?
Why Study Decision Analysis?
Subjective Judgements And Decision Making
The Decision Analysis Process
Where Is Decision Analysis Used
Where Does The Software Fit In?
Where Are We Going From Here?
Summary
Questions And Problems
Case Studies
References
Epilogue
Modeling Decisions
Elements Of Decision Problems
Values And Objectives
Making Money: A Special Objective
Values And The Current Decision Context
Decisions To Make
Sequential Decisions
Uncertain Events
Consequences
The Time Value Of Money: A Special Kind Of Trade-Off
Summary
Questions And Problems
Case Studies
References
Epilogue
Structuring Decisions
Structuring Values
Fundamental And Means Objectives
Getting The Decision Complex Right
Structuring Designs: Influence Diagrams
Influence Diagrams And The Fundamental-Objectives Hierarchy
Using Arcs To Represent Relationships
Some Basic Influence Diagrams
Constructing An Influence Diagram (Optional)
Structuring Decisions: Decision Trees
Decision Trees And Influence Diagrams Compared
Decision Details: Defining Details: Defining Elements Of The Decision
More Decision Details: Cash Flows And Probabilities
Using Precisiontree For Structuring Decisions
Summary
Exercises
Questions And Problems
Case Studies
References
Epilogue
Making Choices
Decision Trees And Expected Monetary Value
Solving Influence Diagrams: Overview
Solving Influence Diagrams: The Details (Optional)
Solving Influence Diagrams: An Algorithm (Optional)
Risk Profiles
Dominance: An Alternative To Emv
Making Decisions With Multiple Objectives
Analysis: One Objective At A Time
Subjective Ratings For Constructed Attribute Scales
Assessing Trade-Off Weights
Analysis: Expected Values And Risk Profiles For Two Objectives
Decision Analysis Using Precisontree
Summary
Exercises
Questions And Problems
Case Studies
References
Epilogue
Sensitivity Analysis
Sensitivity Analysis: A Modeling Approach
Problem Identification And Structure
One-Way Sensitivity Analysis
Tornado Diagrams
Dominance Considerations
Two-Way Sensitivity Analysis
Sensitivity To Probabilities
Two-Way Sensitivity Analysis For Three Alternatives (Optional)
Sensitivity Analysis In Action
Sensitivity Analysis Using Toprank And Precisiontree
Sensitivity Analysis: A Built-In Irony
Summary
Exercises
Questions And Problems
Case Studies
References
Epilogue
Creativity And Decision Making
What Is Creativity? Theories Of Creativity
Chains Of Thought
Phases Of The Creative Process
Blocks To Creativity
Cultural And Environmental Blocks
Value-Focused Thinking For Creating Alternatives
Other Creativity Techniques
Creating Decision Opportunities
Summary
Questions And Problems
Case Studies
References
Epilogue
Modeling Uncertainty
Probability Basics
A Little Probability Theory
Venn Diagrams
More Probability Formulas
Uncertain Quantities
Examples
Decision-Analysis Software And Bayes'' Theorem
Summary
Exercises
Questions And Problems
Case Studies
References
Epilogue
Subjective Probability
Probability: A Subjective Interpretation
Assessing Discrete Probabilities
Assessing Continuous Probabilities
Pitfalls: Heuristics And Biases
Decomposition And Probability Assessment
Experts And Probability Assessment: Pulling It All Together
Coherence And The Dutch Book (Optional)
Constructing Distributions Using Riskview
Summary
Exercises
Questions And Problems
Case Studies
References
Epilogue
Theoretical Probability Models
The Binomial Distribution
The Poisson Distribution
The Exponential Distribution
The Normal Distribution
The Beta Distribution
Viewing Theoretical Distributions With Riskview
Summary
Exercises
Questions And Problems
Case Studies
References
Epilogue
Using Data
Using Data To Construct Probability Distributions
Using Data To Fit Theoretical Probability Models
Fitting Distributions To Data
Using Data To Model Relationships
The Regression Approach
Natural Conjugate Distributions (Optional)
A Bayesian Approach To Regression Analysis (Optional)
Summary
Exercises
Questions And Problems
Case Studies
References
Monte Carlo Simulation
Using Uniform Random Numbers As Building Blocks
General Uniform Distributions
Exponential Distributions
Discrete Distributions
Other Distributions
Simulating Spreadsheet Models Using @Risk
Simulation, Decision Trees, And Influence Diagrams
Summary
Exercises
Questions And Problems
Case Studies
References
Value Of Information
Value Of Information: Some Basic Ideas
Expected Value Of Perfect Information
Expected Value Of Imperfect Information
Value Of Information In Complex Problems
Value Of Information, Sensitivity Analysis, And Structuring
Value Of Information And Nonmonetary Objectives
Value Of Information And Experts
Calculating Evpi And Evii With Precisiontree
Summary
Exercises
Questions And Problems
Case Studies
References
Modeling Preferences
Risk Attitudes
Risk
Risk Attitudes
Investing In The Stock Market, Revisited
Expected Utility, Certainty Equivalents, And Risk Premiums
Keeping Terms Straight
Utility Function Assessment
Risk Tolerance And The Exponential Utility Function
Modeling Preferences Using Precisiontree
Decreasing And Constant Risk Aversion (Optional)
Some Caveats
Summary
Exercises
Questions And Problems
Case Studies
References
Epilogue
Utility Axioms, Paradoxes, And Implications
Axioms For Expected Utility
Paradoxes
Implications
A Final Perspective
Summary
Exercises
Questions And Problems
Case Studies
References
Epilogue
Conflicting Objectives I: Fundamental Objectives And The Additive Utility Function Objectives And Attributes
Trading Off Conflicting Objectives: The Basics
The Additive Utility Function
Assessing Individual Utility Functions
Assessing Weights
Keeping Concepts Straight: Certainty Versus Uncertainty
An Example: Library Choices
Using Software For Multiple-Objective Decisions
Summary
Exercises
Questions And Problems
Case Studies
References
Epilogue
Conflicting Objectives II: Multiattribute Utility Models With Interactions
Multiattribute Utility Functions: Direct Assessment
Independence Conditions
Determining Whether Independence Exists
Using Independence
Additive Independence
Substitutes And Complements
Assessing A Two-Attribute Utility Function
Three Or More Attributes (Optional)
When Independence Fails
Multiattribute Utility In Action: Bc Hydro
Summary
Exercises
Questions And Problems
Case Studies
References
Epilogue
Conclusion And Further Reading
A Decision-Analysis Reading List
Binomial Distribution: Individual Probabilities
Binomial Distribution: Cumulative Probabilities
Poisson Distribution: Individual Probabilities
Poisson Distribution: Cumulative Probabilities
Normal Distribution: Cumulative Probabilities
Beta Distribution: Cumulative Probabilities
Answers To Selected Exercises
Author Index

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