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468 Suggested answers to selected questions (3) p(machine accidentally overfilled) = 0.2963. (4) p(minerals in commercial quantities) = 0.8182. (5) (a) (i) Build new plant (expected NPV = $450 000); (ii) EVPI = $85 000. (b) The company should now expand existing plant (expected NPV = $390 750). (6) (a) (i) Plan for medium sales (expected profit = $164 000); (ii) EVPI = $64 000. (b) The company should still plan for medium sales (expected profit = $152 190). (7) Expected value of test = $399 (subject to rounding). (8) (a) (i) The product should be launched (expected NPV = $18 million); (ii) EVPI = $12 million. (b) EVIl = $5.11 million therefore it is worth test marketing the product. (9) Decision rule: if the light illuminates stop production immediately, i.e. do not take a sample from output (EVII = $150 000 − $150 000 = $0). (10) (a) (i) The expected value of the imperfect information (EVII) from the forecast is: $6000 − $5505 = $495. (ii) Central should not buy the forecast since its cost ($1500) exceeds the EVII, but they should request the customer to reduce electricity consumption. (11) (a) The expected value of the imperfect information (EVII) from the test is: $30 000 − $24 040 = $5960. (c) The expected value of perfect information (EVPI) from the test is $29 400. (12) (a) The expected value of the imperfect information (EVII) from the geological survey is: $50m − $47.503 = $2.50m (subject to rounding). (b) If the survey was perfectly reliable, its expected value would be $6 million. Chapter 13 (9) The chart shows that the deal with a value to the council of 45.65 is efficient in that it would offer gains over the tentative deal to both parties. The table shows that this deal is:
Suggested answers to selected questions 469 Land price: $3 million, No community center, Complete landscaping. Chapter 16 (1) Yes. (2) No. The first row of the matrix implies that Debug is four times more preferable than EAC, while the second row shows that it is six times more preferable.
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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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416 The analytic hierarchy process
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Page 463 and 464: Statistical models of judgment 447
Page 469 and 470: Comparisons 453 ‘broken leg’ cu
Page 471 and 472: Some final words of advice 455 Ques
Page 473 and 474: Some final words of advice 457 of t
Page 475 and 476: References 459 Table 17.1 - Summary
Page 477 and 478: References 461 20. Eisenhart, J. (1
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Page 488 and 489: 472 Index Brown, C.E. 446 Brown, S.
Page 490 and 491: 474 Index HIVIEW 58 Hodgkinson, G.P
Page 492 and 493: 476 Index scenario intervention cas
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