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162 Decision trees and influence diagrams Equip your factory to manufacture calculators? Competitor's calculator prices? Price of component parts? Sales of non-calculator electronic equipment Figure 6.12 – Influence diagrams Equip factory? Yes No Equip half or whole factory? Half Whole Competitor pricing High Medium Low Equip half or whole of factory? High Medium Low (a) Profit this year (b) Component costs Market price to be charged for calculators by your company? Pricing decision High Medium Low Figure 6.13 – Decision tree derived from influence diagram Level of calculator production? Revise this year's executive salary scales? Production decision High Medium Low Outcome A B C
Summary 163 where the resulting decision tree is asymmetric) is more problematic. In these instances, the influence diagram approach to decision tree structuring can be used as a guide only. From our overview of influence diagrams you will have seen that such diagrams aid subsequent structuring of decision trees. They allow the easy insertion of additional acts and events as the decision maker talks through the decision problem with the decision analyst. (See stages 1 and 2 of Figure 6.9.) By themselves, influence diagrams do not aid in the creation of decision options or in the recognition of event possibilities. Such creation and recognition activities perhaps may be best thought of as creative behavior. As we have seen, Fischhoff et al. found that people seem to suffer from ‘out of sight, out of mind’ bias when evaluating the completeness of decision tree-type representations of knowledge. In other words, individual decision makers may be inappropriately content with decision problem representations that are created early in the decision maker/analyst interaction. One major way to combat this tendency is to subject initial problem representations to outside critique by other people with a knowledge of the decision problem domain. Such critiques are readily available in the context of decision conferencing, where those individuals with a stake in a key decision interact with the aid of a decision analyst who acts to facilitate a decision. We will deal with this approach in detail in Chapter 12, where we will focus on the advantages and disadvantages of group decision making. Summary In this chapter we have illustrated the construction of decision trees and the rollback method for identifying the optimal policy. We described an approximation method for dealing with continuous probability distributions within decision trees and summarized some practical applications of decision trees within decision analysis. Finally, we analyzed the process of generating decision tree representation of decision problems and advocated the influence diagram as a key technique to facilitate decision structuring. Despite the benefits of using decision trees some decision analysts counsel against using them too early in the decision process before a broad perspective of the decision problem has been obtained. For example, Chapman and Ward 19 argue that decision trees should often be embedded in a more wide-ranging analysis that includes assessments
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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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Page 128 and 129: 112 Decision making under uncertain
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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
Page 171 and 172: Assessment of decision structure 15
Page 173 and 174: Assessment of decision structure 15
Page 175 and 176: Eliciting decision tree representat
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Page 181 and 182: Exercises 165 other elements of the
Page 183 and 184: Exercises 167 (b) Assuming that Wes
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Page 187 and 188: Exercises 171 failing to meet the p
Page 189 and 190: Exercises 173 why it was rational f
Page 191 and 192: Exercises 175 (11) In the Eagle Mou
Page 193 and 194: References 177 ABC Chemicals are pl
Page 195 and 196: 7 Applying simulation to decision p
Page 197 and 198: Monte Carlo simulation 181 Of cours
Page 199 and 200: Monte Carlo simulation 183 Table 7.
Page 201 and 202: Applying simulation to a decision p
Page 213 and 214: Applying simulation to investment d
Page 221 and 222: Summary 205 Hull 10 ), some of them
Page 223 and 224: Exercises 207 (c) Use your simulati
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Page 227 and 228: 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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