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276 Biases in probability assessment 36. Edmundson, R. H., Lawrence, M. J. and O’Connor, M. J. (1988) The Use of Non-Time Series Information in Sales Forecasting: A Case Study, Journal of Forecasting, 7, 201–211. 37. Goodwin, P. and Wright, G. (1993) Improving Judgmental Time Series Forecasting: A Review of the Guidance Provided by Research, International Journal of Forecasting, 9, 147–161. 38. Goodwin, P. and Wright, G. (1994) Heuristics, Biases and Improvement Strategies in Judgmental Time Series Forecasting, Omega International Journal of Management Science, 22, 553–568. 39. Goodwin, P. (1996) Statistical Correction of Judgmental Point Forecasts and Decisions, Omega International Journal of Management Science, 24, 551–559. 40. Gigerenzer, G. (1994) Why the Distinction between Single Event Probabilities and Frequencies is Important for Psychology (and Vice Versa), in G. Wright and P. Ayton (eds) Subjective Probability, Wiley, Chichester. 41. Sniezek, J. and Buckley, T. (1991) Confidence Depends on Level of Aggregation, Journal of Behavioral Decision Making, 4, 263–272. 42. Kahneman, D. and Lovallo, D. (1993) Timid Choices and Bold Forecasts: A Cognitive Perspective on Risk Taking, Management Science, 39, 17–31. 43. Cooper, A., Woo, C. and Dunkelberger, W. (1988) Entrepreneurs Perceived Chances for Success, Journal of Business Venturing, 3, 97–108. 44. Bolger, F. and Wright, G. (1994) Assessing the Quality of Expert Judgment: Issues and Analysis, Decision Support Systems, 11, 1–24.
10 Methods for eliciting probabilities Introduction We have seen in earlier chapters that subjective probabilities provide a concise and unambiguous measure of uncertainty and they are therefore an important element of many decision models. A number of techniques have been developed to assist the decision maker with the task of making probability judgments and in this chapter we will examine some of the more widely used methods. At the end of the last chapter we gave advice on how to approach probability estimation. This was based on recent research evidence that people prefer to think in terms of the relative frequencies with which events occur, rather than probabilities. Nevertheless, it may not always be possible to assess frequencies for an event when there are no past instances of that event or similar events occurring. Even when such past frequencies do exist, some of the techniques we describe below may be useful. For example, techniques that break the estimation task into smaller parts or use graphs may both help to improve the quality of judgments. It has been found that these techniques are employed most effectively when they are administered by an analyst who uses them as part of an interview with the decision maker (see Spetzler and Stäel von Holstein 1 and Shephard and Kirkwood 2 ). Of course, the assessment process can be time consuming and therefore the importance of sensitivity analysis cannot be overstated. It may reveal, for example, that a very rough assessment of the probabilities in a problem is adequate because the choice of a given course of action is valid for a wide range of probability values. Towards the end of the chapter we will examine the calibration of subjective probability estimates in depth and discuss additional ways in which the process of probability assessment can be improved.
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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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Page 242 and 243: 226 Revising judgments in the light
Page 263 and 264: 9 Biases in probability assessment
Page 265 and 266: Introduction 249 of people watching
Page 267 and 268: The availability heuristic 251 make
Page 269 and 270: The representativeness heuristic 25
Page 275 and 276: The anchoring and adjustment heuris
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Page 279 and 280: Other judgmental biases 263 Other j
Page 281 and 282: Is human probability judgment reall
Page 287 and 288: Exercises 271 Did you receive timel
Page 289 and 290: References 273 have been taken from
Page 291: References 275 18. Hamilton, D. L.
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Page 307 and 308: Assessing probabilities for very ra
Page 309 and 310: Exercises 293 Odds in favor of even
Page 311: References 295 13. Wright, G. and W
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326 Decisions involving groups of i
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328 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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