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14 Appendix Figure 14.6 – Solution to the nine-dot problem (Figure 14.3)
References 375 References 1. Luchins, A. S. and Luchins, E. G. (1959) Rigidity of Behavior, <strong>University</strong>of Oregon Press: Eugene, Oregon. 2. Scheerer, M. (1963) Problem Solving, Scientific American, 208, 118–128. 3. Evans, J. R. (1991) Creative Thinking in the Decision and Management Sciences, South-Western, Cincinnati. 4. de Bono, E. (1992) Serious Creativity, HarperCollins, London. 5. Thaler, R. H. (1999) Mental Accounting Matters, Journal of Behavioral Decision Making, 12, 183–206. 6. Hodgkinson, G. P. and Johnson, G. (1994) Exploring the Mental Models of Competitive Strategists: The Case for a Processual Approach, Journal of Management Studies, 31, 525–551. 7. Barr, P. S., Stimpert, J. L. and Huff, A. S. (1992) Cognitive Change, Strategic Action and Organizational Renewal, Strategic Management Journal, 13, 15–36. 8. Porac, J. F., Thomas, H. and Baden-Fuller, C. (1989) Competitive Groups as Cognitive Communities: The Case of Scottish Knitwear Manufacturers, Journal of Management Studies, 26, 397–416. 9. Johnson, G. (1988) Rethinking Incrementalism, Strategic Management Journal, 9, 75–91. 10. For a review see Hodgkinson, G. P. (in press) The Cognitive Analysis of Competitive Structures: A Review and Critique, Human Relations. 11. Slovic, P. (1972) From Shakespeare to Simon: Speculations – and some Evidence – <strong>About</strong> Man’s Ability to Process Information, Oregon Research Institute Bulletin, 12. 12. For example, Phillips, L. D., Hayes, W. L. and Edwards, W. (1966) Conservatism in Complex Probabilistic Inference, IEEE Transactions in Human Factor in Electronics, 7, 7–18. 13. Pitz, G. F., Downing, L. and Reinhold, H. (1967) Sequential Effects in the Revision of Subjective Probabilities, Canadian Journal of Psychology, 21, 381–393. 14. Winkler, R. L. and Murphy, A. M. (1973) Experiments in the Laboratory and the Real World, Organizational Behavior and Human Performance, 10, 252–270. 15. Ayton, P. and Wright, G. (1994) Subjective Probability: What Should We Believe? in G. Wright and P. Ayton (eds) Subjective Probability, Wiley, Chichester. 16. Tversky, A. and Kahneman, D. (1974) Judgment under Uncertainty: Heuristics and Biases, Science, 185, 1124–1131. 17. Bolger, F. and Harvey, N. (1993) Context Sensitive Heuristics in Statistical Reasoning, Quarterly Journal of Experimental Psychology, 46A, 779–811. 18. Evans, J. St. B. T. (1987) Beliefs and Expectations as Causes of Judgmental Bias, in G. Wright and P. Ayton (eds) Judgmental Forecasting, Wiley, Chichester.
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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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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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Page 340 and 341: 324 Decisions involving groups of i
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Page 371 and 372: 14 Decision framing and cognitive i
Page 373 and 374: How people frame decisions 357 Figu
Page 375 and 376: Imposing imaginary constraints and
Page 377 and 378: Inertia in strategic decision makin
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Page 393 and 394: 15 Scenario planning: an alternativ
Page 395 and 396: Introduction 379 documented in Chap
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Page 399 and 400: Using scenarios in decision making
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Page 419 and 420: Illustrative case study 403 (b) Att
Page 421 and 422: Illustrative case study 405 Stage 4
Page 423 and 424: Illustrative case study 407 importa
Page 425 and 426: Conclusion 409 combinations. Often
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Page 430 and 431: 414 The analytic hierarchy process
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424 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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