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70 Decisions involving multiple objectives: SMART 7. Buede, D. M. and Choisser, R. W. (1984) An Aid for Evaluators of System Design Alternatives, Defense Management Journal, 32–38. 8. Brownlow, S. A. and Watson, S. R. (1987) Structuring Multi-attribute Value Hierarchies, Journal of the Operational Research Society, 38, 309–317. 9. Edwards, W. and Newman, J. R. (1986) Multiattribute Evaluation, in H. R. Arkes and K. R. Hammond (eds) Judgment and Decision Making, Cambridge University Press, Cambridge. 10. Von Winterfeldt, D. and Edwards, W. (1986) Decision Analysis and Behavioral Research, Cambridge University Press, Cambridge. 11. Pöyhönen M., Vrolijk H. and Hämäläinen R. P. (2001) Behavioral and Procedural Consequences of Structural Variation in Value Trees, European Journal of Operational Research, 134, 216–227. 12. Bodily, S. E. (1985) Modern Decision Making, McGraw-Hill, New York. 13. Phillips, L. D. (1984) A Theory of Requisite Decision Models, Acta Psychologica, 56, 29–48. 14. Keeney, R. L. (1992) Value Focused Thinking, Harvard University Press, London. 15. Wright, G. and Goodwin, P. (1999) Rethinking Value Elicitation for Personal Consequential Decisions, Journal of Multi-Criteria Decision Analysis, 8, 3–10. 16. Edwards, W. and Barron, F. H. (1994) SMARTs and SMARTER: Improved Simple Methods for Multiattribute Utility Measurement, Organizational Behavior and Human Decision Processes, 60, 306–325. 17. Belton, V. and Stewart, T. R. (2002) Multiple Criteria Decision Analysis. An Integrated Approach, Kluwer Academic Publishers, Norwell, MA. 18. Roberts, R. and Goodwin, P. (in press) Weight Approximations in Multi- Attribute Decision Models, Journal of Multi-Criteria Decision Analysis.
4 Introduction Introduction to probability In the previous chapter we discussed the analysis of decisions where uncertainty was not considered to be a major factor. However, in many problems the decision maker is not sure what will happen if a particular course of action is chosen. A company that is considering the purchase of an expensive new machine will face uncertainty relating to factors such as the machine’s reliability, life span and resale value. Similarly, an individual who has to decide whether or not to purchase household insurance will be uncertain as to whether his home will be burgled, flooded or damaged by fire. In the next chapter we will be looking at how to analyze decisions which involve uncertainty, but before this we need to consider how the concept of probability can be used to provide a measure of uncertainty. There are a number of ways in which uncertainty can be measured and expressed. The simplest method involves the use of words such as ‘unlikely’, ‘almost impossible’, ‘probable’, ‘doubtful’ and ‘expected’. Unfortunately, it has been found that different people attach very different meanings to these expressions, and even individuals are not consistent over time in their use of them. For example, Moore and Thomas 1 discuss an experiment at a business school where a large number of executives were asked to rank 10 words or phrases in decreasing order of uncertainty. The ranking of the word ‘likely’ ranged from second to seventh, while the ranking for ‘unlikely’ ranged from third to tenth. Numbers offer a much more precise way of measuring uncertainty, and most people will be familiar with the use of odds. While many decision makers may be happy to use expressions such as ‘100 to 1 against’ or ‘evens’, odds do have the disadvantage that they are awkward to handle arithmetically when, for example, we want to determine the chances that a number of different outcomes will occur.
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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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18 How people make decisions involv
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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
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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
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120 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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