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346 Resource allocation and negotiation problems Value Value Value 100 50 100 Union 0 3 7 50 100 Union 0 0 1 0 Do not reinstate staff Pay rise (%) (a) Management Extra days' holiday Union (b) (c) 11 15 Management 2 3 Management Reinstate staff Weights Management 100 Management 50 Management 10 Figure 13.7 – Management and union value functions and weights Union 80 Union 100 Union 50 to management of a particular deal to be measured. The management then made an assessment of the weights they thought that the union would place on similar swings and these are also shown in Figure 13.7. The values to the management and union of any deal could now be calculated. After several meetings, the following tentative agreement had been reached: only a 3% pay award would be granted but employees
An illustrative problem 347 Table 13.3 – Calculation of values for the tentative management–union deal Value Normalized weight Value × weight Management Pay rise of 3% 100 0.625 62.5 3 extra days’ holiday 0 0.3125 0 Reinstatement of staff 0 0.0625 0 Value of deal 62.5 Union Pay rise of 3% 0 0.348 0 3 extra days’ holiday 100 0.435 43.5 Reinstatement of staff 100 0.217 21.7 Value of deal 65.2 would receive an extra 3 days’ holiday per year and the sacked workers would be reinstated. Table 13.3 shows how the value of this deal to the management and the union was calculated. During the negotiations one of the management team had used a computer to calculate the management and union values for all possible deals (assuming, for simplicity, that the percentage increase in pay would be a whole number). The results of these calculations are shown in Figure 13.8. On this graph the efficient frontier represents the set of agreements which are such that no party can improve its position without worsening the position of the other party. This means that if an agreement does not lie on the efficient frontier then other agreements are available which would either offer an improved position to both parties or allow one party to improve its position without doing so at the expense of the other. It can be seen that the tentative deal (indicated by T on the graph) did not lie on the efficient frontier. All the agreements on the frontier between A and B were more efficient, and it was therefore in the interests of both parties to try to improve on T by reaching one of these agreements. After much more bargaining they eventually settled on deal X. This involved a 7% pay rise but only one day’s extra holiday per year, though the staff who had been fired would still be reinstated. Since the deal had a value of 77.4 for the management and 74.9 for the union it represented a clear improvement over the tentative deal for both parties. It is obviously tempting to ask whether decision analysis can be used to determine an ‘optimum’ agreement from those available on the efficient
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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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Case study of a scenario interventi
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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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