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106 Decision making under uncertainty Choose Luxuria Hotel Choose Maxima Center Expected utility = 0.75 Expected utility = 0.5 0.6 High attendance Low attendance 0.4 0.5 High attendance Low attendance 0.5 Payoffs $30000 $11000 $60000 −$10000 Figure 5.3 – The conference organizer’s decision tree with utilities Utility utilities in the same way as the monetary values we are able to identify the course of action which leads to the highest expected utility. Choosing the Luxuria Hotel gives an expected utility of: 0.6 × 0.85 + 0.4 × 0.6 = 0.75 Choosing the Maxima Center gives an expected utility of: 0.5 × 1.0 + 0.5 × 0 = 0.5 Thus the business woman should choose the Luxuria Hotel as the venue for her exhibition. Clearly, the Maxima Center would be too risky. It may be useful at this point to establish what expected utilities actually represent. Indeed, given that we have just applied the concept to a one-off decision, why do we use the term expected utility? To see what we have done, consider Figure 5.4(a). Here we have the business woman’s decision tree with the original monetary sums replaced by the lotteries which she regarded as being equally attractive. For example, receiving $30 000 was considered to be equivalent to a lottery offering a 0.85 probability of $60 000 and a 0.15 probability of −$10 000. Obviously, receiving $60 000 is equivalent to a lottery ticket offering $60 000 for certain. You will see that every payoff in the tree is now expressed in 0.85 0.6 1.0 0
Single-attribute utility 107 Choose Luxuria Hotel Choose Maxima Center Choose Luxuria Hotel Choose Maxima Center 0.6 High attendance Low attendance 0.4 0.5 High attendance Low attendance 0.75 0.25 0.5 0.5 0.5 (a) (b) $60000 −$10000 $60000 −$10000 0.85 0.15 0.6 0.4 1.0 0 0 1.0 $60000 −$10000 $60000 −$10000 $60000 −$10000 $60000 −$10000 Figure 5.4 – A demonstration of how expected utility reduces the decision to a simple choice between lotteries terms of a probability of obtaining either the best outcome ($60 000) or the worst outcome (−$10 000). Now, if the business woman chooses the Luxuria Hotel she will have a 0.6 probability of finishing with a profit which she perceives to be equivalent to a lottery ticket offering a 0.85 probability of $60 000 and a
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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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Page 72 and 73: 56 Decisions involving multiple obj
Page 88 and 89: 72 Introduction to probability Beca
Page 90 and 91: 74 Introduction to probability prac
Page 92 and 93: 76 Introduction to probability we w
Page 94 and 95: 78 Introduction to probability whet
Page 96 and 97: 80 Introduction to probability give
Page 98 and 99: 82 Introduction to probability prob
Page 100 and 101: 84 Introduction to probability deci
Page 102 and 103: 86 Introduction to probability that
Page 104 and 105: 88 Introduction to probability inte
Page 106 and 107: 90 Introduction to probability subj
Page 108 and 109: 92 Introduction to probability (5)
Page 110 and 111: 94 Introduction to probability (a)
Page 112 and 113: 96 Decision making under uncertaint
Page 114 and 115: 98 Decision making under uncertaint
Page 116 and 117: 100 Decision making under uncertain
Page 159 and 160: 6 Decision trees and influence diag
Page 161 and 162: Constructing a decision tree 145 be
Page 163 and 164: Determining the optimal policy 147
Page 165 and 166: Decision trees and utility 149 succ
Page 167 and 168: Decision trees involving continuous
Page 169 and 170: Practical applications of decision
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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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17 Alternative decision-support sys
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Expert systems 429 generally agreed
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Expert systems 431 that knowledge e
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Expert systems 433 (1) That the sub
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Expert systems 435 information syst
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Expert systems 437 to focus on ‘t
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Expert systems 439 On the basis of
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Is there any intention/expectation
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Expert systems 443 described above
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Expert systems 445 body of knowledg
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Statistical models of judgment 447
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Comparisons 453 ‘broken leg’ cu
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Some final words of advice 455 Ques
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Some final words of advice 457 of t
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References 459 Table 17.1 - Summary
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References 461 20. Eisenhart, J. (1
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Chapter 3 Suggested answers to sele
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Suggested answers to selected quest
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472 Index Brown, C.E. 446 Brown, S.
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474 Index HIVIEW 58 Hodgkinson, G.P
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476 Index scenario intervention cas
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