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472 Index Brown, C.E. 446 Brown, S.W. 435 Brownlow, S.A. 33 buck passing 367 Buckley, T. 269 Budescu, D.V. 284 Buede, D.M. 30, 32, 132 Bunn, D.W. 132, 267, 284, 293 business-as-usual scenario 387, 399 business idea 383–4 calibration 270, 287–9 card sorting 439 Casey, C. 451 certainty axiom 89 certainty-equivalence approach 116–18 Chalos, P. 451 Chapman, C. 163, 297 Chapman, L.J. 253 Chapman, L.P. 253 checklists 303 Choisser, R.W. 32 citation bias 266 classical approach to probability 73–4 cognitive illusions 379 cognitive inertia 360–1, 362–9 Cohan, D. 153 coherence in probability assessment 285–7, 288 compensatory heuristic 16 competitive advantage 383 complementary events 78–9 complete ordering axiom 113 compound lottery axiom 114, 115 conditional probability 79–80 conditional sampling 204 conditioning phase of probability assessment 278 conditions dilemma 329 Condorcet’s paradox 316 confirmation bias 364 conflict theory 367 conjunction fallacy 257–8, 268 conjunctive events 259–60 conservatism 263, 363, 371 consistency in probability assessment 285–7, 288 constraints, imaginary 359–60 context focusing 439 contextual information 267 continuity axiom 113 continuous probability distribution 85 Cooper, A. 269 coping patterns 367 Corner, J.L. 119 Corner, P.D. 119 Corrigan, B. 449 cost–benefit trade-off 44–6 covariation, biased assessment of 263–4 creativity 4, 303, 355–7 cumulative distribution function (CDF) 86, 192–3, 282 Dawes, R.M. 448, 449, 450, 451 de Bono, Edward 359 De Groot, M.H. 313 de Neufville, R. 123 Decanal Engineering Corporation 123–4 decidability axiom 49 decision aids 429 decision analysis 452 applications 5–11 divide and conquer orientation of 4 partial 5 role of 3–5 decision-analytic representation 154–9 decision conferencing 6, 7, 9, 10, 323–5, 454 decision frame 357–9 decision hierarchy 414–15 decision-support systems 429 decision table 96 decision trees 7, 143–64, 409, 452 and continuous probability distributions 150–2 assessment of structure 154–9 constructing 144–6 elicitation of representations 159–63 optimal policy determination 147–9 practical applications 152–4 utility and 149 defensive avoidance 367 Delphi technique 321–3, 400 democratic process 395 dependence relationship modeling 204–5 dependent event 80 direct assessment of probabilities 279–81 direct rating 35–7 discrete probability distribution 84 disjoint events 76 disjunctive events 260–1
Index 473 distinctive competencies 383 Downing, L. 363 DPL 149 driving forces method 387–93 Drury, C. 198 Duda, R.O. 430 Dunkelberger, W. 269 Dunning, D.J. 144 Dyer, J.S. 422 Edison, Thomas 360 Edwards, W. 30, 34, 41, 46, 51, 52, 54, 55, 56, 120, 290, 315, 363 efficient frontier 45 effort–accuracy framework 23 Eggleton, I.R.C. 255 Eilon, S. 204 Einhorn, H.J. 265, 289, 449 Eisenhart, J. 436 electronic data interchange (EDI) 442 elimination by aspects (EBA) 19 EQUITY 11, 330, 341–2, 343 ES 2 436 Esser, J.K. 320 Evans, J.R. 364 event trees 290–1 events, definition of 72 EXEL Logistics 9 exhaustive events 76 EXMAR 436, 437 expected monetary value (EMV) criterion 98–102, 147 expected utility 106–8, 155 expected value of imperfect information (EVII) 230–4, 236 expected value of perfect information (EVPI) 227–9, 234 expected values 87–9 EXPERT CHOICE 413, 414, 417, 418, 419, 420, 423 expert knowledge 429–34 expert system shells 432 expert systems 10, 427–47, 453–5 definition 427–8 expert knowledge in 431–43 financial services applications 438–44 fraud detection systems 444–5 marketing applications 434–7 point-of-sale advice-giving systems 445–7 Extended Pearson-Tukey (EP-T) approximation 151–2, 153 extreme-world method 380–3 false assumptions 359–60 Farquahar, P.H. 116 fast and frugal heuristic 16, 24 fault trees 157–9, 291–2 Fawkes, T.R. 204 Ferrell, W.R. 310, 313, 315, 317, 322 finite upper and lower bounds for value axiom 49 Fischhoff, B. 156, 157–9, 289 forced relationships 303 Ford, Henry 361 forward chaining 431 forward to the past scenario 396–7, 398 frame analysis worksheet 369–71, 373 frame blindness 358, 360–1 framing 453 framing effects 368 free enterprise scenario 397, 398 French, S. 115 frequency view of probabilities 267–70 Galton, Sir Francis 256 Gigerenzer, Gerd 16, 23, 24, 267, 268, 269, 379 Giuliano, T. 365 Goldberg, L.R. 449 Goodwin, P. 57 Goslar, M.D. 435 graph drawing in probability assessment 282–4 group processes 309–25 structured 321–3 unstructured 320–1 groupthink 320, 367–8, 378 growth mechanism 383 Harker, P.T. 422 Harvey, N. 364 Hayes-Roth, R. 430 Hershey, J.C. 117, 118 Hertz, D.B. 153, 189, 196, 204 Hespos, R.F. 203 heuristics anchoring and adjustment 258–62, 364, 371 availability 251–3 compensatory 16 fast and frugal 16, 24 with multiple objectives 16–23 non-compensatory 16, 18, 19 recognition 16–17 representativeness 253–8 hindsight bias 289
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Decision Analysis for Management Ju
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Decision Analysis for Management Ju
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To Mary and Josephine, Jamie, Jerom
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viii Contents Chapter 16 The analyt
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x Foreword real problems, and it is
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xii Preface accountancy, where rece
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1 Introduction Complex decisions Im
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The role of decision analysis 3 you
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Applications of decision analysis 5
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Overview of the book 11 and profit.
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References 13 8. Walls, M. R., Mora
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16 How people make decisions involv
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28 Decisions involving multiple obj
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72 Introduction to probability Beca
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74 Introduction to probability prac
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76 Introduction to probability we w
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78 Introduction to probability whet
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80 Introduction to probability give
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82 Introduction to probability prob
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84 Introduction to probability deci
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86 Introduction to probability that
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88 Introduction to probability inte
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90 Introduction to probability subj
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92 Introduction to probability (5)
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94 Introduction to probability (a)
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96 Decision making under uncertaint
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98 Decision making under uncertaint
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100 Decision making under uncertain
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6 Decision trees and influence diag
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Constructing a decision tree 145 be
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Determining the optimal policy 147
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Decision trees and utility 149 succ
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Decision trees involving continuous
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Practical applications of decision
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Assessment of decision structure 15
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Assessment of decision structure 15
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Eliciting decision tree representat
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Eliciting decision tree representat
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Summary 163 where the resulting dec
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Exercises 165 other elements of the
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Exercises 167 (b) Assuming that Wes
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Exercises 169 is not hired, given t
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Exercises 171 failing to meet the p
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Exercises 173 why it was rational f
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Exercises 175 (11) In the Eagle Mou
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References 177 ABC Chemicals are pl
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7 Applying simulation to decision p
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Monte Carlo simulation 181 Of cours
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Monte Carlo simulation 183 Table 7.
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Applying simulation to a decision p
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Applying simulation to investment d
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Summary 205 Hull 10 ), some of them
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Exercises 207 (c) Use your simulati
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Exercises 209 For each factor the f
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Exercises 211 (b) Suppose that your
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References 213 6 million Therefore
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216 Revising judgments in the light
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9 Biases in probability assessment
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Introduction 249 of people watching
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The availability heuristic 251 make
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The representativeness heuristic 25
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The anchoring and adjustment heuris
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The anchoring and adjustment heuris
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Other judgmental biases 263 Other j
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Is human probability judgment reall
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Exercises 271 Did you receive timel
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References 273 have been taken from
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References 275 18. Hamilton, D. L.
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10 Methods for eliciting probabilit
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Preparing for probability assessmen
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Consistency and coherence checks 28
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Assessment of the validity of proba
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Assessment of the validity of proba
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Assessing probabilities for very ra
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Exercises 293 Odds in favor of even
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References 295 13. Wright, G. and W
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298 Risk and uncertainty management
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310 Decisions involving groups of i
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330 Resource allocation and negotia
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14 Decision framing and cognitive i
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How people frame decisions 357 Figu
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Imposing imaginary constraints and
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Inertia in strategic decision makin
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Studies in the psychological labora
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Overcoming inertia? 369 it is diffi
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Discussion questions 373 frame anal
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References 375 References 1. Luchin
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15 Scenario planning: an alternativ
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Introduction 379 documented in Chap
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Scenario construction: the extreme-
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Using scenarios in decision making
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Using scenarios in decision making
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Scenario construction: the driving
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Case study of a scenario interventi
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Table 15.1 - The components of the
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Illustrative case study 403 (b) Att
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Illustrative case study 405 Stage 4
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Illustrative case study 407 importa
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Conclusion 409 combinations. Often
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References 411 2. Van der Heijden,
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414 The analytic hierarchy process
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Page 438 and 439: 422 The analytic hierarchy process
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Page 475 and 476: References 459 Table 17.1 - Summary
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Page 490 and 491: 474 Index HIVIEW 58 Hodgkinson, G.P
Page 492 and 493: 476 Index scenario intervention cas
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