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336 Resource allocation and negotiation problems 100 75 50 25 0 East region Best strategy Worst strategy 100 50 0 West region Best strategy Worst strategy Figure 13.2 – A comparison of the East and West regions’ value scales for market share For short-term profit the weights were calculated directly from the group’s original monetary estimates. Thus the largest swing between the worst and best short-term profits offered by the different strategies was in the East (a difference of $33 million) and this swing was allocated a weight of 100. The swing between the worst and best level of profits in the North was only $9 million. Since this was only 27.3% of the largest swing (i.e. 9/33 × 100) the within-criterion weight allocated to short-term profits in the North was 27.3. The other profit weights were calculated in a similar way. For the other benefits the within-criterion weights were elicited using a different approach. In the case of ‘Market share’, for example, the group was asked to imagine the package which would have the least desirable effect on market share. This would be the package which involved reducing the operation in the North to three outlets, closing down the operation in the West and maintaining the status quo in the other regions. They were then asked: if they could choose to change just one of the strategies in this package, which change would lead to the greatest improvement in market share? After some debate, the group thought that this improvement would be achieved if a switch was
The main stages of the analysis 337 Weight Weight Weight 100 0 100 0 100 0 27.27 N 70 N 10 N Profit 39.39 W Market share 50 W Risk 100 E 100 E 90.91 Figure 13.3 – The within-criterion weights for the furniture company problem 20 made in the East region from maintaining the status quo (strategy 1) to expanding to 10 outlets (strategy 3). This meant that ‘Market share’ in the East region was assigned a weight of 100. The facilitator then asked the group to identify, from the strategies available in the other regions, the change which would lead to the second largest improvement in market share. The group said that they would switch from reducing the operation in the North (strategy 1) to maintaining the status quo in that region (strategy 2). After further discussion, they said that they felt that the improvement in market share resulting from this switch would only be 70% of that which would be W 100 E S 60 S 30 S
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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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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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