Index
- Activism, 289
- Actuarial risk, 218–219
- Actuarial science/tables, 22, 85–86, 279
- Actuaries (risk management horseman), 82–86
- Additive weighted scores, 165–166
- Agreed-on policy statements, usage, 121
- AIG, problems, 8, 85, 104
- Aleatory uncertainty, 129
- Algorithm aversion, 195, 198, 203
- Algorithms, 194–203
- Ambiguity, impact, 170–173
- Amtrak, derailments/collisions, 4
- Analysis, term (usage), 13
- Analysis placebo, 5, 41–42, 100–102, 189
- Analysts, Monte Carlo simulations (usage), 224–228
- Analytic hierarchy process (AHP), 33, 54, 189–190
- Analytic solutions, 262–263
- Analytica, 316
- Anchoring, 269, 271
- Andreassen, Paul, 42
- Antifragility, 205
- Aon Global Risk Consulting, 25–27, 45–46, 331
- Applied information economics (AIE), 228, 295, 328
- Arbitrary points, usage, 72–73
- Armstrong, J. Scott, 261–262
- Arthur Andersen, demise, 96, 99–100
- Artifacts, 155–160
- Assessment, term (usage), 13
- Assessors, risk aversion, 181
- Auditors, usage, 99
- Audits, usage, 29–30
- Augliere, Reed, 175
- Backtesting, 317–318
- Baez, John C., 185
- Barings Bank, problems, 282
- Baseline failure rates, 304
- Baxter International, Inc., 39–40, 229
- Bayesian analysis/method/view, 131, 276–277, 300e, 305
- Bayesian networks, 250
- Bayesian statistics, 276–278
- Bayes' theorem/inversion, 297–301
- Bears, swans (contrast), 193
- Bear Stearns, failure, 8
- Bearden, David, 265
- Behavioral economics, 95
- Behavior, incentivization, 335–337
- Berra, Yogi, 223
- Bernoulli distribution, 308
- Best-fit correlation, 246–247
- Best practices, 5, 39
- Beta distribution, 301–302, 309–310
- Bias, 138, 140, 174
- Bickel, J. Eric, 182
- Big experiment, 50–52
- Binary probabilities, forecast validations, 318–319
- Binomial distribution, 300–301
- Binomial probability, 301
- Black, Fischer, 94
- Black-Scholes equation, 94–95
- Black swans, 203–209
- Boeing 737 MAX, crashes/failures, 5–7
- Bohn, Christopher, 45–46, 219, 324
- Bohr, Niels, 323
- Boolean distribution, 308
- Box, George, 75
- Bratvold, Reidar, 182
- Brier core/scores, 332–334, 333e
- Broomell, Stephen, 172
- Brunswik, Egon, 155–156, 158, 184–185, 313–314
- Buckets, range forecasts, 319
- Budescu, David, 170–173, 180–182
- Calibrated culture, incentives, 331–337
- Calibrated estimators, usage, 319–320
- Calibrated individuals, uncalibrated individuals (comparison), 145e
- Calibrated probability assessment, 66–67, 212
- Calibration, 226, 266, 269–272
- Capital investments, 229
- Cascade failure, 240–241, 283
- Catastrophes, 297, 303
- Catastrophic overconfidence, 142–150
- Catastrophic risks, 305
- Cause and effect, direct evidence, 50, 52–53
- Certain monetary equivalent (CME), 122–128, 133, 336
- Certified distributions, 329
- Chamberlain, Thomas C., 223
- Chance, misconception, 140
- Chartered enterprise risk actuary (CERA) certification, 86
- Checklists, usage, 29–30
- Chief decision analysis officer (CDAO), 325
- Chief information officers (CIOs), 228–229
- Chief probability officer (CPO), 325
- Chief risk officer (CRO), 217, 324, 325
- Chi-square test, 238
- Chomel, Auguste Francois, 163
- Churchill, Winston, 86
- Clemen, Bob, 171–172, 313
- Close analogy fallacy, 236
- Closed-form solutions, 262–263
- Cognitive Neuroscience Society study, 159
- Coin flip, 113, 119
- Collaborative, term (usage), 230–231
- Common mode failure, 5–8, 241, 243
- Complementary cumulative probability function, 69
- Completeness, check, 50, 54–56
- Complexity index, 265
- Compliance remediation, 289
- Component testing, 50, 53–55
- Conditional probability, 303
- Conditional robust Bayesian method, 304e
- Confidence interval (CI), 237–238, 263, 267
- Confiscation, expropriation, nationalization, and deprivation (CEND), 220
- Conjunction fallacy, 141
- Consensus, building, 47, 101
- Consequence (risk component), 168
- Construction engineering, definition, 118–119
- Contingent losses, ambiguity (FASB rules), 172
- Contractual risk transfer, 288
- Controlled studies, reliance, 206
- Control Objectives for Information and Related Technology (CobIT), 102, 168, 185
- Control self-assessment (CSA), 99
- Cooke, Roger, 314–315, 334
- Coopers & Lybrand, 96–98
- Correlated data, examples, 245e
- Correlated variable, 243
- Correlation coefficient, 247e
- Correlation degrees, 244
- Correlations, 243–247, 311–312
- Cost, measurable unit (usage), 185
- Coupon insurance, 220
- Covert, Ray, 264–265
- Cox, Dennis William, 339
- Cox, Tony, 173–176, 180–183, 190
- Credit default swaps (CDSs), position, 85
- Credit risk, 165
- Criticality matrix, 9
- Critical, term (usage), 87
- Crowe Horwath, 330
- Crystal Ball, 225, 316, 329
- Culture. 14. 47. 331–337
- Currency risk, 165–166, 247
- Cyberattacks, 15
- Cyber risk, 56
- Cybersecurity, 13, 33, 65, 157, 209, 227
- Dantzig, George, 91–92
- Data, 67, 202, 272–280, 321
- Dawes, Robyn, 139, 184–185
- Death, cause, 141
- Debiasing, 266
- Decision analysis (DA), 77, 89, 123, 324
- Decision-maker, 127e, 132, 296
- Decision making, 40–44, 153, 214
- Decision making, issues, 342–343
- Decision theory/psychology, 88, 130
- Decisions, quality control, 138, 343
- Decomposition, 54, 262, 264, 305–307
- Deductive reasoning, 279–280
- Deep learning, 250
- Deepwater Horizon Offshore Oil Spill, 4, 7
- Defined procedure, usage, 28
- DeLoach, Jim, 45–46, 99–100, 228
- Deloitte, 35
- Delphi technique, 42
- Deming, W.E., 45, 97
- Dependencies, 311–312
- Deterministic, term (usage), 32
- Devlin, Keith, 217
- Dietvorst, Berkeley, 195–198
- di Finetti, Bruno, 130, 211
- Digital Millennium Copyright Act of 1998, 289
- Dillon-Merrill, Robin, 151–152, 190, 303
- Disaster recovery and business continuity planning (DR/BCP), 12
- Disasters, 7, 297, 303
- Disaster, shape, 236–243
- Distributions, 237–239, 237e, 300–302, 308–311, 329
- Dodd-Frank Wall Street Reform and Consumer Protection Act (2009), 24, 103
- Domino effect, 283
- Dow Jones Industrial Average (DJIA), 146, 241, 242e
- Drucker, Peter, 21, 45, 97
- Drug manufacturing, outsourcing (risk), 36–40
- Drug production, outsourcing, 39–40
- Dunning, David, 44
- Dunning-Kruger effect, 44, 210
- Duration, measurable unit (usage), 185
- Dynamic online source, usage, 331
- Dynamic trading, complexity, 335
- Earthquake frequency/severity, power-law distributions, 240e
- Economic literature, risk/probability (presence), 93e
- Economist Intelligence Unit (EIU), 25, 26
- Economists (risk management horseman), 82, 90–96
- Emotional stimulation, NIH study, 159
- Empirica Capital LLC, 207
- Empirical data/inputs, usage, 76, 294–305
- Empirical observation, 94
- Enron, 15, 96, 172
- Enterprise Leadership Team (ELT), 341
- Enterprise resource planning (ERP), 51, 230
- Enterprise risk management (ERM), 11, 18, 55, 227, 339–340
- Equivalent bet, 267–269, 271, 276
- Equivalent urn (EQU), 269
- Estimates, 54, 260–262, 318
- Estimation problem, observation, 261
- Evans, Dylan, 176
- Even-more-common common mode failure, 7
- Events, 136, 219, 283, 311–312, 334
- Exceedance, probability, 69
- Excel, 225, 251–252, 264, 301–302, 315–316
- Exercise price, 93
- Expected loss column, 307
- Expected opportunity loss (EOL), 248–249, 294–295
- Expected value of information (EVI), 295
- Expected value of perfect information (EVPI), 248–249, 294–295
- Experience, features, 136–137
- Experts, 64–67, 135, 208, 294, 314
- Experts inconsistency, reduction, 313–314
- Expiration date, 93
- Exponential utility function, 125e
- Exposures, selection processes, 288
- Exsupero ursus fallacy (Beat the Bear), 195, 196, 200–203, 205–206, 210, 259
- Extraorganizational issues, 337–339
- Failure, 14–17, 298, 300, 300e, 330
- Failure modes, effects and criticality analysis (FMECA), 99
- Fallacy of close analogy, 236
- Fear, uncertainty, and doubt (FUD), sale, 100–101
- Federal Financial Institutions Examinations Council (FFIEC), 168, 185
- Feedback, 137, 241, 243, 266, 271
- Fermi, Enrico, 261, 263, 317
- Feynman, Richard P., 35, 150–151, 293, 304, 317
- Finance management, Knight (impact), 114–118
- Financial Accounting Standards Board (FASB) rules, 171–172
- Financial crisis (2008), 8, 96, 229
- Financial models, usage, 236–243
- Financial portfolio risk, 56
- Financial reporting, risk valuation, 172
- Fischhoff, Baruch, 139, 145, 153, 191
- Fisher, Ronald A., 128, 212
- Forecaster, 332–334, 333e
- Forecasts, 226, 313, 318–319, 326, 333
- Formal errors, 50, 54
- Formal inspections indicator, 37
- Fractals, usage, 207
- Fractionated expertise, 149
- Framing, 158
- France, Clemens J., 113
- Frankendistributions, 311
- Franklin, Jim, 329
- Freeman, Andrew, 336
- Friedman, Milton, 92
- Frontline Systems Incline Village, 316
- Fukushima Daiichi Nuclear Disaster, 4, 7, 15, 17, 201
- Gaussian probability distribution, 236, 242
- General Data Protection Regulation (GDPR), 24
- Global probability model (GPM), 228–230, 328e, 336–339
- Goodness-of-fit tests, 238
- Gossett, William Sealy, 311
- Governance risk and compliance (GRC), 12
- Great Recession (2008/2009), 3–4, 201
- Gruenberger, Fred J., 185
- Gulf Canada, 99
- Hammer, Mike, 97
- Health care, patient risks (evaluation), 13
- Heat map, 30, 168
- Heisenberg, Werner, 113
- Hershey Foods Corp., 230
- Hester, John, 340–341
- Heuer, Dick, 173, 181
- Heuristics, 138, 181
- High-risk nations, credit risk insurance, 220
- Hippocratic Oath, 163
- Historical data, 178e, 208, 226, 312
- History of histories, 208
- Hoblitt, Richard, 235
- Homo Economus, 94–95
- Horizontal decomposition, 306–307
- Howard, Ron, 89, 123, 125, 188
- Hoye, Steve, 232–233, 251–252
- Human errors/biases, 54, 106
- Human estimates, errors, 196
- Hume, David, 317
- Hurricane frequency/severity, power-law distributions, 240e
- Hurricane Katrina, impact, 7, 15, 201
- Hybrid deterministic methods, 258
- Hybrid distributions, 311
- I-knew-it-all-along phenomenon, 153
- Impact scale, 167e, 180e
- Inconsistencies, 76, 155–160
- Independence, presumption, 174, 179
- Indifferent criterion independence, 187
- Inductive reasoning, 279–280
- Information engineering methodology, 98
- Information Systems Audit and Control Association (ISACA), 102
- Information Technology (IT), 116, 157e, 166, 169, 177–178
- Information Technology Governance Institute (ITGI), 102
- Information value, 294–295
- Infrastructure, 62, 314
- Initial benchmarks, data usage, 272–280
- Inputs, addition, 76
- Institute for Operations Research and the Management Sciences (INFORMS), 190
- Institutional factors, 107
- Insufficient information response, usage, 215–216
- Insurance, 219, 288
- Interconnections, consideration, 76
- Intergovernmental Panel on Climate Change (IPCC), 170–171, 173
- International Olympic Committee, insurance, 219
- International Standards Organization (ISO), 24, 30, 168
- Interquartile range, 319
- Intervals, presumption, 174, 177–179
- Intuitive approaches, sale, 101–102
- Inventory management systems, reliability, 49
- Inversion, Bayes' theorem (usage), 299
- Investments, 13, 42
- Irrational preferences/belief, 127, 141
- ISO 31000, 103
- Kahneman, Daniel, 43, 126, 130, 137, 140–143, 149, 153, 158, 186, 204, 206, 269, 274, 278, 296, 343
- Keynes, John Maynard, 90–91, 111–112
- Klein, Gary, 43–45, 137, 269, 283, 330
- Klein's premortem, 269
- Knight, Frank, 90–91, 111–118, 132–133
- Knutson, Brian, 159
- Kolmogorov-Smirnov test (K-S test), 238
- Kong, Y.S., 287
- KPMG survey, 25–30, 32, 36, 46
- Kruger, Justin, 44
- Laplace, Pierre Simon de, 257, 278, 280, 301, 321
- Launch failure, chance, 300
- Lehman Brothers, 8, 339
- Lewis, Michael, 198
- Lichtenstein, Sarah, 139, 145, 269
- Lie detection, 42
- Life insurance, 288
- Likelihood, 30, 169, 172
- Likelihood scale, 167e, 170
- Linear programming, 91–92
- Liquid assets/reserves, 289
- Logistic regression analysis, performance, 314
- Lognormal distribution, 76, 263, 311
- Log t distribution, 311
- Long-term capital management (LTCM), 207, 229
- Loss contingencies, recognition, 172
- Loss exceedance curve (LEC), 68, 69e, 71, 122, 283–285, 341
- Loss probability, 132
- Loss reduction, 74
- Ludic fallacy, 204–205
- Lumina Decision Systems, 316
- Lump sum, preference, 336
- MacGregor, Donald G., 261–262
- MacMillan, Fiona, 342
- Maintenance, requirement (determination), 13
- Management, 10, 83, 96–103
- Management consulting services (MCS), 96
- Mandelbrot, Benoit, 207
- Manhattan Project, 68, 261, 263
- Market-based incentives, 334–335
- Markowitz, Harry, 91–93, 114–115
- Martin, James, 97–98
- Massey, Cade, 195
- Mathematica, 316
- Mathematical misconceptions, 209–217
- Matrices, examples, 164–169
- Maximum bearable risk, 133
- Measurement, 248–250, 297
- Meehl, Paul, 198–199, 203, 205–206, 260–261
- Memory-based experience, 136
- Mental math, 139–142
- Merton, Robert C., 94, 207
- Meta-history, usage, 322
- Metropolis, Nicholas, 68, 263
- Meyer, Wayne, 61
- Microsoft, 316
- Mission failure (predictor), complexity (usage), 265
- Mitigation, return, 73–74
- Modelers/modeling, 231, 285, 317–322
- Modern portfolio theory (MPT), 48, 92–96, 115, 207–208
- Monetary loss, subjective assignation, 65
- Monte Carlo applications, 225
- Monte Carlo-based methods, 250–252, 264–266
- Monte Carlo model, 87–88, 179, 252, 296, 305–312, 327–331
- Monte Carlo simulations, 28, 67, 71, 202, 216–217, 224–228
- Monte Carlo tools, 295, 315, 316e
- Morgenstern, Oscar, 88, 123–124, 186, 188
- Mount St. Helens fallacy/risk analysis, 235, 247, 275, 320–321
- Multi-attribute utility theory (MAUT), 33, 54, 187–189
- Multi-criteria decision-making (MCDM), 33, 187, 189
- Multiple objective decision analysis (MODA), 187
- Multiplicative risk matrices, 165, 179
- Multivariate regression, 155–156
- Naive scoring methods, development, 185
- NASA, 8, 264–266, 303, 304
- National Institute of Standards & Technology (NIST), 24, 30, 102, 166–167, 175, 337
- National Transportation Safety Board (NTSB), 7
- Near misses, 303–305, 304e
- Near miss interpretation paradox, 152
- New York Stock Exchange (NYSE), 146
- Nietzsche, Frederick, 163
- NIST 800-30, 102
- Nobel, Alfred, 204
- Non-modelers, 231
- Nonquantitative matrix, mapping, 281e
- Nonrisks, filtration, 281
- Normal distribution (Gaussian distribution), 237e, 236–239, 308
- Normal probability distribution, 236
- Numbers, trust (avoidance), 139–142
- Numerical scales, usage, 30e
- Observed near miss, 304e
- Office building, solutions, 337–339
- One-for-one substitution, 62–64, 259, 263–264, 280, 305
- Operational probability, 211
- Operational risk, 228, 288
- Operations research (OR), 87, 91
- Opportunity loss (OL), 248
- Options, derivative type, 93
- Options theory (OT), 48, 94–95, 207–208, 229
- Oracle, 316, 329
- Ordinal scales, addition/multiplication, 55
- Organizational management, 324–327
- Organizational Project Management Maturity Model (OPM3), 103
- Outage/disruption, cost (equation), 306
- Overconfidence, 76, 143, 149–154
- Overestimating, consequences, 232
- Palisade Corporation, 316
- Pandit, Vikrim, 245
- Paradigm shift, 186
- Partition dependence, 174, 180–181
- Past events, fear/anger (involvement), 160
- Patton, George, 323
- Peak end rule, 139–140
- Peak energy user, impact, 240
- Pearson, Karl, 156
- Performance, 57–59, 314–315
- Peters, Tom, 97
- Phase-gate analysis, 38
- Pilot tolerance curves, creation, 326
- Pitch, impact, 96–103
- Placebo, 41
- Plato, 317
- Po, Han-Hui, 172
- Poisson distribution, 310–311
- Political risk, 165
- Positive feedback, 241
- Posterior probability, 277
- Post-mortem, 283
- Power law, 239, 240e, 241, 310, 311
- Power tie, impact, 96–103
- Prediction markets, 334–355
- Premortem, 283, 330
- Prequantitative risk analysis, 114
- President's Management Agenda (PMA), 24
- Private Securities Litigation Reform Act of 1995, 289
- Probabilistic functionalism, 155
- Probabilistic inputs, requirement, 266
- Probabilistic risk analysis (PRA), 87–88, 95–96, 105
- Probability (probabilities), 93e, 128–133, 210–212, 259
- Probability density function (pdf), 299
- Probability distributions, usage, 340
- Probability of probabilities, 301–302
- Probability-weighted average, 143
- Productivity improvements, 320–321, 334
- Product liabilities, 65, 288
- Professional societies, growth, 338–339
- Project duration, 320
- Project failure risk, example, 120e
- Project management, 65, 114–118, 227
- Project Management Body of Knowledge (PMBoK), 102, 116, 118, 168, 185
- Project Management Institute (PMI), 102, 116, 118, 132, 337
- Project management professionals (PMPs), certification, 116
- Project portfolio management (PPM), 11
- Project portfolios, 13
- Project Risk Analysis & Management Guide (PRAM Guide), 116–117
- Project risk management (PRM), 11
- Prospect theory, 126
- Protiviti, 25–28, 45–46, 99, 228, 331
- Proven technical proficiency, 37
- Pseudo random number generator (PRNG), 69–70, 264, 307
- Psychological diagnosis, 41–42
- Qualitative procedure, quantitative procedure (contrast), 27–28
- Qualitative risk matrix model, 245
- Qualitative scales, behavioral research, 54
- Quality control, 106–107
- Quantitative analysts, errors, 258
- Quantitative matrix, 64e
- Quantitative models, 54, 202, 230–236, 341–343
- Quantitative representation, nonquantitative matrix (mapping), 281e
- Quantitative risk models/analysis, 223, 229, 342
- R and RStudio, 316
- R Foundation for Statistical Computing, 316
- RAND Corporation, 88, 91–92, 185
- Random deliverable generator (DRG), 97
- Random sampling error, 264
- Random survey (example), normal distribution (usage), 236–238
- Random value, selection, 71
- Range compression/forecasts, 174–177, 319
- Rank reversal, absence, 187
- Rasmussen, Norman C., 87
- Rationalism, 317
- Red Baron effect, 204
- Redundant risks, filtration, 281
- Reference class, 274–275, 278–280
- Repetition, 266, 271
- Representativeness bias, 140
- Retailers, list (creation), 274
- Return on control, equation, 74
- Return on investment (ROI), 57, 175, 185
- Return on mitigation (RoM), 285
- Right question, answering, 50, 56–57
- Risk, 21–24, 27e, 131–134, 280–290, 330–331
- appetite/impact, 121, 173, 260
- aversion, 159, 174, 181
- components/representation, 168, 182
- confusion/uncertainty, 106, 109, 110
- defining, 8, 9, 65, 117, 131, 257, 284
- economic literature, 93e
- elimination/loss, 57, 119–121
- event, materialization, 65
- map, numerical/verbal scales (usage), 30e
- matrix, 23, 30–31, 99, 136, 182–183, 250, 280, 284
- paradox, 228–236
- perception/preferences, 160
- probability, subjective assignation, 65
- quantification, 233
- subjective judgments, human errors (avoidance), 106
- uncertainty, contrast, 110
- Risk analysis, 12–14, 230, 238–239, 257–258, 298
- Risk assessment, 26–34, 54, 84, 309, 323
- RiskLens, 316
- Risk management, 7–16, 21, 25–26, 105–107, 285–290, 290e
- apocalypse, 81
- change, power tie/pitch (impact), 96–103
- clarification, 324
- evolution/solutions/language, 257–266
- four horsemen, 81–83, 103–105, 104e
- improvement, 90, 193, 258–259
- measurement/methods/process/evaluations, 29, 48–52
- process, 7, 47–48
- profession, growth, 337–338
- skepticism, 3
- success-failure spectrum, 58–59
- Risk Management Guide for Information Technology Systems (NIST 800-30), 166–167
- Risk/return analysis/model, 287–288, 324–325, 327, 330
- Risk Reward Limited, 339
- Risk Solver Engine, 316, 329
- Risk tolerance, 72–73, 121–128, 133, 159, 260, 325
- Robust Bayesian method, 300e, 301, 304e
- Robust prior, 277
- Rogers Commission Report, 150
- Roychowdhury, Vwani, 154
- Rubin, Robert, 246
- Rule of succession, 278
- Rumsfeld, Donald, 133
- Russell, Bertrand, 279–280
- Saaty, Thomas, 189–190
- Sagan, Carl, 3
- Sales goal, reaching (failure/risk), 284
- Sam Savage Probability Management, 316
- Samples, 141–142
- Sarbanes-Oxley, 338
- Sarbanes-Oxley Act of 2002, 24, 172
- SAS Corporation, 316
- Savage, Leonard J., 89, 128, 186
- Savage, Sam, 89, 186–187, 209, 251, 259, 324–325, 329
- Scales, 173–178, 176e, 178e
- Scenarios, set (generation), 314
- Schoemaker, Paul J.H., 135
- Scholes, Myron, 94, 207
- Schuyler, John, 233, 285, 331
- Scores, 164–169, 176e, 177, 183–186, 189–191
- Scoring, 169, 177, 183–191
- Security threats, protections level, 13
- Self-assessments, 44–48
- Sharpe Ratio, 56
- Shuttle disaster, chance, 304e
- Silver, Nate, 196–198, 201, 207
- Simkin, Mikhail, 154
- Simmons, Joseph P., 195
- Simulated loss column modification, 307
- Simulations, 71, 216–217
- SIPMath, 316
- Slovic, Paul, 139, 153
- Smith, Adam, 322
- Smith, Fredrick, 35
- Spinoza, Benedict de, 317
- Spreadsheet errors, 331
- SPSS Inc., 316
- Stage-gate analysis, 38
- Stakeholders, risk management, 47, 326–327
- Standard deviation, 273
- Standard & Poor's 500 (S&P500), daily price drop, 241–242, 242e
- Statement of Actuarial Opinion, 337
- Statistical models, usage, 260–261
- Statistical Research Group (SRG), 86
- Statistical tests, misconceptions, 212–216
- Stochastic information packets (SIPs), 329
- Stochastic library units with relationships preserved (SLURPS), 329
- Stop-gate analysis, 38, 229
- Straw man quantitative model, 18, 61, 75–77
- Stressed-system failure, 243
- Strict uncertainty, 133
- Structural model, 95
- Structured approaches, sale, 101
- Student t distribution, 311
- Subjective estimates, 226, 312–315
- Subjective judgment, 76, 136
- Subjective preferences, 186–189
- Subjective risks, methods (problems), 106
- Subject matter experts (SMEs), 231, 266, 270–272, 285, 296, 330, 334
- Substitution, checking, 280–285
- Succession, 278, 321
- Supercriticality, 87
- Supply chains, 13, 65
- Swans, bears (contrast), 193
- Taleb, Nassim, 203–209, 224, 236–237, 279–280
- Technology adoption rates, 320
- Terry, Thompson, 342
- Test group, 213
- Testimonial, 48, 101
- Tetlock, Philip, 199, 206, 315, 335
- Thaler, Richard, 139
- Thomas, Philip, 182
- Thorp, Edward, 206–207
- Threat (risk component), 168
- Time scales, change, 321
- Total risk, reduction, 287
- Tracking, 317–318, 320
- Training requirements, determination, 325–326
- Transitivity, 187
- Triangular distribution, 308–309
- True/false predictions/questions, 144e, 266–267, 319, 333
- Truncated distributions, 311
- Trustmark Mutual Holding Company, 339–341
- Twain, Mark, 209–210
- Tversky, Amos, 137–138, 140–143, 158, 186, 269–270
- Ulam, Stanislaw, 68, 263
- Uncalibrated individuals, calibrated individuals (comparison), 145e
- Uncertainty, 109–113, 132–135, 237, 258–266, 324
- Uncertain variables, 249
- Underestimating, consequences, 232
- Unexpected risk, 284
- Uninformative prior (robust prior), 277
- Union Carbide, disaster, 282
- United Airlines flight 232, crash/deaths, 6
- Unknown unknowns, 133
- Unquantifiable randomness, 114
- Upside risk, 114
- Utility, 124, 188
- Value at risk (VaR), 95–96, 251
- Values, combination (usage), 314
- Variable, 247, 297
- Variance, 114–115, 118
- Vector quantity, project failure expression, 120e
- Verbal scales, usage, 30e
- Vertical decomposition, 306
- Volatility, 115–116, 322
- Voltaire, 3, 109, 193
- Von Neumann, Jon, 68, 88, 89, 123–124, 186, 188, 193
- Von Richthofen, Manfred (Red Baron), 154
- Vulnerability (risk component), 168
- Z decompositions, 307
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.