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38 Decisions involving multiple objectives: SMART function. This will enable us to estimate the values of any office area between the most and least preferred. There are several methods which can be used to elicit a value function, but one of the most widely applied is bisection. This method requires the owner to identify an office area whose value is halfway between the least-preferred area (400 ft 2 ) and the mostpreferred area (1500 ft 2 ). Note that this area does not necessarily have to correspond to that of one of the offices under consideration. We are simply trying to elicit the owner’s preferences for office areas in general, and having obtained this information we can then use it to assess his preference for the specific office areas which are available to him. Initially, the owner suggests that the midpoint area would be 1000 ft 2 . This implies that an increase in area from 400 ft 2 to 1000 ft 2 is just as attractive as an increase from 1000 ft 2 to 1500 ft 2 . However, after some thought he rejects this value. The increases from smaller areas will, he reasons, reduce overcrowding and so be much more attractive than increases from larger areas which would only lead to minor improvements. He is then offered other candidates for the midpoint position (for example, 900 ft 2 and 600 ft 2 ), but rejects these values as well. Finally, he agrees that 700 ft 2 has the midpoint value, so v(700) = 50. Having identified the midpoint value, the decision maker is now asked to identify the ‘quarter points’. The first of these will be the office area, which has a value halfway between the least-preferred area (400 ft 2 ) and the midpoint area (700 ft 2 ).Hedecidesthatthisis500ft 2 , so v(500) = 25. Similarly, we ask him to identify an area which has a value halfway between the midpoint area (700 ft 2 )andthebestarea (1500 ft 2 ). He judges this to be 1000 ft 2 , which implies that v(1000) = 75. We now have the values for five floor areas and this enables us to plot the value function for office size, which is shown in Figure 3.3. This value function can be used to estimate the values for the actual areas of the offices under consideration. For example, the Bilton Village office has an area of 550 ft 2 and the curve suggests that the value of this area is about 30. A similar method can be applied to the attribute ‘closeness to customers’. This attribute has been represented by the variable ‘distance from town center’ and the value function is shown in Figure 3.4. Note that the greater the distance from the town center, the lower the value will be. The curve also suggests that a move from 0 to 2 miles from the town center is far more damaging to business than a move from 6 to 8 miles. The values identified for the seven offices in terms of ‘office area’ and ‘closeness to customers’ are shown in Table 3.2 (see page 39).
Measuring how well the options perform on each attribute 39 Value 100 75 50 25 0 400 500 700 1000 1500 Office floor area (ft 2 ) Figure 3.3 – Constructing a value function for office floor area Value 100 75 50 25 0 0 2 4 6 8 Distance from town center (miles) Figure 3.4 – A value function for distance from customers Table 3.2 – Values and weights for the office location problem Office Attribute Weight A B C D E F G Closeness 32 100 20 80 70 40 0 60 Visibility 26 60 80 70 50 60 0 100 Image 23 100 10 0 30 90 70 20 Size 10 75 30 0 55 100 0 50 Comfort 6 0 100 10 30 60 80 50 Car parking 3 90 30 100 90 70 0 80 Aggregate Benefits 80.8 39.4 47.4 52.3 64.8 20.9 60.2
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Decision Analysis for Management Ju
Page 5 and 6: Decision Analysis for Management Ju
Page 7: To Mary and Josephine, Jamie, Jerom
Page 10 and 11: viii Contents Chapter 16 The analyt
Page 12 and 13: x Foreword real problems, and it is
Page 14 and 15: xii Preface accountancy, where rece
Page 17 and 18: 1 Introduction Complex decisions Im
Page 19 and 20: The role of decision analysis 3 you
Page 21 and 22: Applications of decision analysis 5
Page 27 and 28: Overview of the book 11 and profit.
Page 29: References 13 8. Walls, M. R., Mora
Page 32 and 33: 16 How people make decisions involv
Page 44 and 45: 28 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
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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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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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