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26 How people make decisions involving multiple objectives References 1. Simon, H. A. (1982) Models of Bounded Rationality, MIT Press, Cambridge, MA. 2. Gigerenzer, G., Todd, P. M. and the ABC Research Group (1999) Simple Heuristics that Make Us Smart, Oxford <strong>University</strong> Press, Oxford. 3. Tversky, A. (1969) Intransitivity of Preferences, Psychological Review, 76, 31–48. 4. Note that when the lexicographic strategy is only employed after the recognition heuristic has failed to discriminate between options, because they are both recognized, it is referred to as the ‘Take the Best’ heuristic. 5. Fishburn, P. (1991) Nontransitive Preferences in Decision Theory, Journal of Risk and Uncertainty, 4, 113–124. 6. Tversky, A. (1972) Elimination by Aspects: A Theory of Choice, Psychological Review, 79, 281–299. 7. Simon, H. A. (1955) A Behavioral Model of Rational Choice, Quarterly Journal of Economics, 69, 99–118. 8. Shafir, E., Simonson, I. and Tversky, A. (1993) Reason-Based Choice, Cognition, 49, 11–36. 9. Shafir, E. (1993) Choosing versus Rejecting: Why Some Options are Both Better and Worse than Others, Memory and Cognition, 21, 546–556. 10. Tversky, A. and Shafir, E. (1992) The Disjunction Effect in Choice under Uncertainty, Psychological Science, 3, 305–309. 11. Simonson, I., Carmon, Z. and O’Curry, S. (1994) Experimental Evidence on the Negative Effect of Product Features and Sales Promotions on Brand Choice, Marketing Science, 13, 23–40. 12. Payne, J. W., Bettman, J. R. and Johnson, E. J. (1993) The Adaptive Decision Maker, Cambridge <strong>University</strong> Press, Cambridge.
3 Decisions involving multiple objectives: SMART Introduction As we saw in the last chapter, when decision problems involve a number of objectives unaided decision makers tend to avoid making trade-offs between these objectives. This can lead to the selection of options that perform well on only one objective, or the rejection of relatively attractive options because their good performance on several objectives is not allowed to compensate for poor performance elsewhere. A laptop computer is rejected because of its slow processor speed, despite its compactness, reliability and low price. A supplier is chosen because of his low price, despite his slow delivery times and poor after-sales service. These problems arise because the unaided decision maker has ‘limited information-processing capacity’ (Wright 1 ). When faced with a large and complex problem, there may be too much information to handle simultaneously so the decision maker is forced to use simplified mental strategies, or heuristics, in order to arrive at a choice. This chapter will explore how decision analysis can be used to support decision makers who have multiple objectives. As we stated in Chapter 1, the central idea is that, by splitting the problem into small parts and focusing on each part separately, the decision maker is likely to acquire a better understanding of the problem than that which would be achieved by taking a holistic view. It can also be argued that, by requiring a commitment of time and effort, analysis encourages the decision maker to think deeply about the problem, enabling a rationale, which is explicit and defensible, to be developed. After such analysis the decision maker should be better able to explain and justify why a particular option is favored. The methodology outlined in this chapter is underpinned by a set of axioms. We will discuss these towards the end of the chapter, but, for the moment, we can regard them as a set of generally accepted propositions or ‘a formalization of common sense’ (Keeney 2 ). If the decision maker
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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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