Making hard decisions with decision tools

Title:

Personal Author:

Clemen, Robert T.

Publication Information:

Pacific Grove, CA : Duxbury/Thomson Learning, 2000

ISBN:

9780534365974

General Note:

Accompanies text with the same title : (HD30.23 C53 2001)

Subject Term:

Decision making

Decision making -- Computer programs

Decision tools

Added Author:

Reilly, Terence

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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.

Preface	p. xxi
Chapter 1 Introduction to Decision Analysis	p. 1
Gypsy Moths and the Oda	p. 1
Why Are Decisions Hard?	p. 2
Why Study Decision Analysis?	p. 3
Subjective Judgments and Decision Making	p. 5
The Decision-Analysis Process	p. 5
Requisite Decision Models	p. 8
Where Is Decision Analysis Used?	p. 8
Where Does the Software Fit In?	p. 9
Where Are We Going from Here?	p. 11
Section 1 Modeling Decisions	p. 19
Chapter 2 Elements of Decision Problems	p. 21
Values and Objectives	p. 21
Making Money: A Special Objective	p. 22
Values and the Current Decision Context	p. 23
Boeing's Supercomputer	p. 24
Decisions to Make	p. 25
Sequential Decisions	p. 26
Uncertain Events	p. 27
Consequences	p. 29
The Time Value of Money: A Special Kind of Trade-Off	p. 30
Larkin Oil	p. 33
Chapter 3 Structuring Decisions	p. 43
Structuring Values	p. 44
Hiring a Summer Intern	p. 44
Fundamental and Means Objectives	p. 46
Getting the Decision Context Right	p. 51
Structuring Decisions: Influence Diagrams	p. 52
Influence Diagrams and the Fundamental-Objectives Hierarchy	p. 54
Using Arcs to Represent Relationships	p. 55
Some Basic Influence Diagrams	p. 57
The Basic Risky Decision	p. 57
Imperfect Information	p. 58
Sequential Decisions	p. 61
Intermediate Calculations	p. 63
Constructing an Influence Diagram (Optional)	p. 65
Toxic Chemicals and the Epa	p. 65
Some Common Mistakes	p. 67
Multiple Representations and Requisite Models	p. 69
Structuring Decisions: Decision Trees	p. 69
Decision Trees and the Objectives Hierarchy	p. 71
Some Basic Decision Trees	p. 72
The Basic Risky Decision	p. 72
Imperfect Information	p. 74
Sequential Decisions	p. 74
Decision Trees and Influence Diagrams Compared	p. 76
Decision Details: Defining Elements of the Decision	p. 76
More Decision Details: Cash Flows and Probabilities	p. 78
Defining Measurement Scales for Fundamental Objectives	p. 79
Using Precision Tree for Structuring Decisions	p. 83
Constructing a Decision Tree for the Research-and-Development Decision	p. 85
Constructing an Influence Diagram for the Basic Risky Decision	p. 91
Chapter 4 Making Choices	p. 111
Texaco Versus Pennzoil	p. 111
Decision Trees and Expected Monetary Value	p. 115
Solving Influence Diagrams: Overview	p. 119
Solving Influence Diagrams: The Details (Optional)	p. 121
Solving Influence Diagrams: An Algorithm (Optional)	p. 127
Risk Profiles	p. 128
Cumulative Risk Profiles	p. 132
Dominance: An Alternative to EMV	p. 133
Making Decisions with Multiple Objectives	p. 137
The Summer Job	p. 138
Analysis: One Objective at a Time	p. 140
Subjective Ratings for Constructed Attribute Scales	p. 140
Assessing Trade-Off Weights	p. 142
Analysis: Expected Values and Risk Profiles for Two Objectives	p. 143
Decision Analysis Using PrecisionTree	p. 146
Decision Trees	p. 146
Influence Diagrams	p. 150
Multiple-Attribute Models	p. 154
Chapter 5 Sensitivity Analysis	p. 174
Eagle Airlines	p. 174
Sensitivity Analysis: A Modeling Approach	p. 175
Problem Identification and Structure	p. 176
One-Way Sensitivity Analysis	p. 179
Tornado Diagrams	p. 180
Dominance Considerations	p. 181
Two-Way Sensitivity Analysis	p. 183
Sensitivity to Probabilities	p. 184
Two-Way Sensitivity Analysis for Three Alternatives (Optional)	p. 188
Investing in the Stock Market	p. 189
Sensitivity Analysis in Action	p. 192
Heart Disease in Infants	p. 192
Sensitivity Analysis Using TopRank and PrecisionTree	p. 193
Top Rank	p. 193
PrecisionTree	p. 201
Sensitivity Analysis: A Built-In Irony	p. 206
Chapter 6 Creativity and Decision Making	p. 217
What Is Creativity?	p. 218
Theories of Creativity	p. 219
Chains of Thought	p. 219
Phases of the Creative Process	p. 220
Blocks to Creativity	p. 222
Framing and Perceptual Blocks	p. 222
The Monk and the Mountain	p. 222
Making Cigars	p. 223
Value-Based Blocks	p. 225
Cultural and Environmental Blocks	p. 227
Ping-Pong Ball in a Pipe	p. 227
Organizational Issues	p. 229
Value-Focused Thinking for Creating Alternatives	p. 230
Fundamental Objectives	p. 230
Means Objectives	p. 230
Transportation of Nuclear Waste	p. 231
The Decision Context	p. 232
Other Creativity Techniques	p. 233
Fluent and Flexible Thinking	p. 233
Idea Checklists	p. 233
Brainstorming	p. 236
Metaphorical Thinking	p. 236
Other Techniques	p. 238
Creating Decision Opportunities	p. 239
Section 2 Modeling Uncertainty	p. 247
Chapter 7 Probability Basics	p. 249
A Little Probability Theory	p. 250
Venn Diagrams	p. 250
More Probability Formulas	p. 251
Uncertain Quantities	p. 256
Discrete Probability Distributions	p. 257
Expected Value	p. 259
Variance and Standard Deviation	p. 260
Covariance and Correlation for Measuring Dependence (Optional)	p. 262
Continuous Probability Distributions	p. 266
Stochastic Dominance Revisited	p. 267
Stochastic Dominance and Multiple Attributes (Optional)	p. 268
Probability Density Functions	p. 269
Expected Value, Variance, and Standard Deviation: The Continuous Case	p. 270
Covariance and Correlation: The Continuous Case (Optional)	p. 271
Oil Wildcatting	p. 272
John Hinckley's Trial	p. 278
Decision-Analysis Software and Bayes' Theorem	p. 280
Chapter 8 Subjective Probability	p. 295
Uncertainty and Public Policy	p. 295
Probability: A Subjective Interpretation	p. 297
Accounting for Contingent Losses	p. 298
Assessing Discrete Probabilities	p. 299
Assessing Continuous Probabilities	p. 303
Pitfalls: Heuristics and Biases	p. 311
Tom W.	p. 311
Representativeness	p. 312
Availability	p. 313
Anchoring and Adjusting	p. 314
Motivational Bias	p. 314
Heuristics and Biases: Implications	p. 314
Decomposition and Probability Assessment	p. 315
Experts and Probability Assessment: Pulling It All Together	p. 321
Climate Change at Yucca Mountain, Nevada	p. 324
Coherence and the Dutch Book (Optional)	p. 326
Constructing Distributions Using RISK view	p. 328
Chapter 9 Theoretical Probability Models	p. 352
Theoretical Models Applied	p. 353
The Binomial Distribution	p. 354
The Poisson Distribution	p. 358
The Exponential Distribution	p. 361
The Normal Distribution	p. 363
The Beta Distribution	p. 369
Viewing Theoretical Distributions with RISK view	p. 373
Discrete Distributions	p. 374
Continuous Distributions	p. 376
Chapter 10 Using Data	p. 398
Using Data to Construct Probability Distributions	p. 398
Histograms	p. 399
Empirical CDFs	p. 400
Halfway Houses	p. 400
Using Data to Fit Theoretical Probability Models	p. 404
Fitting Distributions to Data	p. 405
Using Data to Model Relationships	p. 412
The Regression Approach	p. 414
Estimation: The Basics	p. 417
Estimation: More than One Conditioning Variable	p. 424
Regression Analysis and Modeling: Some Do's and Don't's	p. 429
Regression Analysis: Some Bells and Whistles	p. 432
Regression Modeling: Decision Analysis versus Statistical Inference	p. 435
An Admonition: Use with Care	p. 435
Natural Conjugate Distributions (Optional)	p. 436
Uncertainty About Parameters and Bayesian Updating	p. 437
Binomial Distributions: Natural Conjugate Priors for p	p. 439
Normal Distributions: Natural Conjugate Priors for [mu]	p. 440
Predictive Distributions	p. 443
Predictive Distributions: The Normal Case	p. 444
Predictive Distributions: The Binomial Case	p. 444
A Bayesian Approach to Regression Analysis (Optional)	p. 445
Chapter 11 Monte Carlo Simulation	p. 459
Fashions	p. 460
Using Uniform Random Numbers as Building Blocks	p. 463
General Uniform Distributions	p. 464
Exponential Distributions	p. 465
Discrete Distributions	p. 466
Other Distributions	p. 466
Simulating Spreadsheet Models Using @RISK	p. 466
Multiple Output Models	p. 475
Distributions on Parameters (Optional)	p. 481
Dependent Input Variables (Optional)	p. 482
Simulation, Decision Trees, and Influence Diagrams	p. 486
Chapter 12 Value of Information	p. 496
Investing in the Stock Market	p. 496
Value of Information: Some Basic Ideas	p. 497
Probability and Perfect Information	p. 497
The Expected Value of Information	p. 499
Expected Value of Perfect Information	p. 500
Expected Value of Imperfect Information	p. 502
Value of Information in Complex Problems	p. 508
Value of Information, Sensitivity Analysis, and Structuring	p. 509
Seeding Hurricanes	p. 510
Value of Information and Nonmonetary Objectives	p. 511
Value of Information and Experts	p. 512
Calculating EVPI and EVII with PrecisionTree	p. 512
EVPI	p. 512
EVII	p. 516
Section 3 Modeling Preferences	p. 525
Chapter 13 Risk Attitudes	p. 527
E. H. Harriman Fights for the Northern Pacific Railroad	p. 528
Risk	p. 529
Risk Attitudes	p. 531
Investing in the Stock Market, Revisited	p. 533
Expected Utility, Certainty Equivalents, and Risk Premiums	p. 535
Keeping Terms Straight	p. 539
Utility Function Assessment	p. 539
Assessment Using Certainty Equivalents	p. 540
Assessment Using Probabilities	p. 541
Gambles, Lotteries, and Investments	p. 543
Risk Tolerance and the Exponential Utility Function	p. 543
Modeling Preferences Using PrecisionTree	p. 546
Sensitivity Analysis of Risk Tolerance	p. 550
Decreasing and Constant Risk Aversion (Optional)	p. 551
Decreasing Risk Aversion	p. 551
An Investment Example	p. 552
Constant Risk Aversion	p. 553
Some Caveats	p. 556
Chapter 14 Utility Axioms, Paradoxes, and Implications	p. 571
Preparing for an Influenza Outbreak	p. 571
Axioms for Expected Utility	p. 572
Paradoxes	p. 578
Implications	p. 582
Implications for Utility Assessment	p. 582
Managerial and Policy Implications	p. 584
A Final Perspective	p. 586
Chapter 15 Conflicting Objectives I: Fundamental Objectives and the Additive Utility Function	p. 598
Objectives and Attributes	p. 600
Trading Off Conflicting Objectives: The Basics	p. 602
Choosing an Automobile: An Example	p. 602
The Additive Utility Function	p. 604
Choosing an Automobile: Proportional Scores	p. 606
Assessing Weights: Pricing Out the Objectives	p. 607
Indifference Curves	p. 608
Assessing Individual Utility Functions	p. 610
Proportional Scores	p. 610
Ratios	p. 612
Standard Utility-Function Assessment	p. 614
Assessing Weights	p. 614
Pricing Out	p. 615
Swing Weighting	p. 615
Lottery Weights	p. 618
Keeping Concepts Straight: Certainty versus Uncertainty	p. 620
An Example: Library Choices	p. 621
The Eugene Public Library	p. 621
Using Software for Multiple-Objective Decisions	p. 628
Chapter 16 Conflicting Objectives II: Multiattribute Utility Models with Interactions	p. 644
Multiattribute Utility Functions: Direct Assessment	p. 645
Independence Conditions	p. 647
Preferential Independence	p. 647
Utility Independence	p. 648
Determining Whether Independence Exists	p. 648
Using Independence	p. 650
Additive Independence	p. 651
Substitutes and Complements	p. 654
Assessing a Two-Attribute Utility Function	p. 654
The Blood Bank	p. 655
Three or More Attributes (Optional)	p. 659
When Independence Fails	p. 660
Multiattribute Utility in Action: BC Hydro	p. 661
Strategic Decisions at BC Hydro	p. 661
Chapter 17 Conclusion and Further Reading	p. 675
A Decision-Analysis Reading List	p. 676
Appendixes	p. 679
A Binomial Distribution: Individual Probabilities	p. 680
B Binomial Distribution: Cumulative Probabilities	p. 688
C Poisson Distribution: Individual Probabilities	p. 696
D Poisson Distribution: Cumulative Probabilities	p. 701
E Normal Distribution: Cumulative Probabilities	p. 706
F Beta Distribution: Cumulative Probabilities	p. 710
Answers to Selected Exercises	p. 719
Credits	p. 721
Author Index	p. 722
Subject Index	p. 725

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