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308 Risk and uncertainty management Av. donation in N – 1st letter %reply in N %reply to reminder in N Av. donation in S – 1st letter %reply in S Av. donation from reminder in N %reply to reminder in S Av. donation from reminder in S %reply in C Av. donation in C – 1st letter %reply to reminder in C Av. donation from reminder in C Figure 11.7 – Tornado diagram for AB Charity References 2 2 5 6 1 2 4 4 2 5 1 −20000 30000 80000 130000 180000 230000 5 Surplus $ 1. Chelst, K. and Bodily, S. E. (2000) Structured Risk Management: Filling a Gap in Decision Analysis Education, Journal of the Operational Research Society, 51, 1420–1432. 2. Rothkopf, M. H. (1996) Models as Aids to Thought, Interfaces, 26, 64–67. 3. Chapman, C. and Ward, S. (2002) Managing Project Risk and Uncertainty: A Constructively Simple Approach to Decision Making, Wiley, Chichester. This book contains many other practical ideas for exploring and managing uncertainty. 4. de Bono, E. (1990) Lateral Thinking: A Textbook of Creativity, Penguin, London. 5. Evans, J. R. (1991) Creative Thinking in the Decision and Management Sciences, South-Western, Cincinnati. 5 11 4 10 12 14 14 16 11 15 12 17
12 Decisions involving groups of individuals Introduction This book has spent many chapters looking at the use of decision analysis for improving decision making. As we have seen, the major inputs to the analysis are subjective probabilities, utilities and decision tree structures. So far, we have focused on the individual decision maker, but important decisions are often made by accountable managers working within small groups of people, most, or all, of whom have information that could be utilized in the decision-making process. Often individuals may differ in their subjective probabilities of events, their utilities of outcomes or in their perception of the subsequent actions available as the pattern of actions, events and outcomes is unfolded into the future. If the opinion and values of individuals differ, how should the differences be resolved? Obviously, several individuals who are involved in decision making bring together a larger fund of experience, knowledge and creative insights. It is intuitively reasonable that the chances of overlooking possible events and possible courses of action are diminished in group decision making. Indeed, the synergy of individuals may make the overall quality of the group decision greater than the sum of the parts. The creation of juries, panels and cabinets as ways of reaching decisions can be seen to be based on this premise. This chapter describes and evaluates ways of combining individual judgments to produce ‘improved’ judgments. There are essentially two approaches to the problem: mathematical and behavioral aggregation (although the approaches can be combined). Mathematical aggregation, which we will discuss first, involves techniques such as the calculation of a simple average of the judgments of the individual group members. In behavioral aggregation a group judgment is reached by members of the group communicating with each other either in open discussion or via a more structured communication process.
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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 representativeness heuristic 25
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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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