Project Economics and Decision Analysis, Vol. 2 Book

	Preface	xiii
1.	Introduction
	Handling Uncertainty in Capital Investments	4
	Industry Practice Over the Years	6
	Classification of Decision Situations	7
	Terminology Used in Decision Analysis	8
	The Decision Analysis Cycle	11
	Advantages of Decision Analysis	16
	Misconceptions Regarding Decision Analysis	17
	Applications of Decision Analysis	19
	Typical Industry Risks	20
	Questions and Problems	24
2.	Statistics and Probability Concepts
	Descriptive Statistics	28
	Measures of Central Tendency	30
	Mean, Median, and Mode	31
	Geometric, Harmonic, and Quadratic Mean	35
	Weighted Average	39
	Choice of a Suitable Average	42
	Measures of Variability	44
	Range, Variance, and Standard Deviation	45
	Mean Absolute Deviation	46
	Coefficient of Variation	47
	Descriptive Statistics from Grouped Data	50
	Mean, Median, and Mode	52
	Geometric and Harmonic Mean	53
	Variance and Standard Deviation	53
	Graphical Presentation of Data	56
	Other Measures	62
	Quartiles	62
	Dociles and Percentiles	64
	Coefficient of Peakedness and Skewness	66
	Spreadsheet Application	66
	Understanding Probability Concepts	70
	Objective Probability	72
	Classical Approach	72
	Empirical Approach	75
	Subjective Probability	77
	Modern Approach	77
	Operations on Event Sets	81
	Characteristics of Events	82
	Rules of Probability	84
	Complementation Rule	85
	Addition Rule	85
	Multiplication Rule	87
	Probability Table and Probability Tree	92
	Baye's Rule	94
	Theoretical Probability Distributions	99
	Discrete Probability Distributions	101
	Binomial Probability Distribution	101
	Multinomial Probability Distribution	105
	Hypergeometric Probability Distribution	107
	Poisson Probability Distribution	110
	Continuous Probability Distributions	113
	Normal Probability Distribution	114
	Lognormal Probability Distribution	122
	Uniform Probability Distribution	126
	Triangular Probability Distribution	128
	Spreadsheet Application	132
	Excel Built-in Functions	132
	Introducing BestFit	138
	BestFit Add-in	139
	Questions and Problems	142
3.	Expected Value and Decision Trees
	Expected Value Concepts	153
	Expected Value of Random Variable	154
	Standard Deviation of Random Variable	155
	Expected Monetary Value (EMV)	157
	Sensitivity Analysis	163
	Expected Profitability Index (EPI)	166
	Performance Index	172
	Expected Opportunity Loss (EOL)	172
	Mean-Variance and Stochastic Dominance	174
	Meaning and Interpretation of Expected Value	181
	Characteristics of Expected Value	185
	Value of Information	186
	Expected Value of Perfect Information (EVPI)	187
	Expected Value of Imperfect Information (EVII)	190
	Decision Trees	197
	Decision Tree Convention	198
	Guidelines for Designing Trees	200
	Solving a Decision Tree	201
	Constructing Risk Profiles	207
	Advantages of Decision Trees	208
	Spreadsheet Applications	209
	Excel Built-in Functions	209
	Introducing Precision Tree	210
	PrecisionTree Add-in	212
	Constructing the Tree	213
	Risk Profile	219
	Sensitivity Analysis	220
	Questions and Problems	227
4.	Incorporating Attitudes Toward Risk
	The Expected Utility Theory	237
	Typical Attitudes toward Risk	237
	The Axioms of Utility	241
	Risk Tolerance	244
	Certainty Equivalent and Risk Premium	246
	Assessing the Utility Function	250
	Mathematical Representation of Utility Functions	255
	Approximation to Certainty Equivalent	262
	Risk Aversion	263
	Expected Utility Decision Criteria	268
	Spreadsheet Applications	269
	Fitting the Utility Curve	269
	Finding the Certainty Equivalent	273
	Critical Risk Tolerance	274
	PrecisionTree and Utility Functions	276
	Questions and Problems	280
5.	Determining Venture Participation
	Gambler's Ruin	287
	Working Interest and RAV	289
	Based on Exponential Risk Aversion	289
	Optimum Working Interest	290
	Apparent Risk Tolerance	290
	Breakeven Working Interest	291
	Based on Hyperbolic Risk Aversion	293
	Parabolic Approximation to Cozzolino's RAV	298
	Modified Risk-Adjusted Value	300
	Portfolio Balancing and RAV	303
	Portfolio Balancing--Cozzolino RAV Formula	304
	Portfolio Balancing--Parabolic RAV Formula	305
	Spreadsheet Applications	307
6.	Simulation in Decision Analysis
	Designing the Simulation Model	314
	Real Applications of Simulation	315
	Steps in Simulation Modeling	316
	Random Sampling Methods	322
	Random Numbers	322
	Monte Carlo Sampling	324
	Latin Hypercube Sampling	326
	Behind-The-Scene Calculations	328
	Recognizing Dependence on Input Variables	333
	Simulating Total Dependence	338
	Simulating Diffuse Dependence	339
	Spreadsheet Applications	347
	Using Excel for Simulation	348
	Generating Random Numbers in Excel	348
	Probability Distributions in Excel	349
	Introducing @RISK	352
	Loading @RISK	354
	Developing the Simulation Model	355
	Running the Simulation	359
	Analyzing the Output	361
	Handling Dependency on @RISK	370
	Combining Precision Tree and @RISK	373
	Afterword	379
Appendix A	Probability Tables	383
Appendix B	Contents of the Accompanying CD-ROM	395
	Index	399