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Car won't start All other problems Mischievious acts or vandalism Other engine problems Ignition system defective Fuel system defective Starting system defective Battery charge insufficient 1. Theft or breakage of vital part (e.g. battery) 1. Oil too thick 2. Pistons frozen 3. Poor compression 2. Siphoning of gas 3. Disruption of wiring 1. Coil faulty 2. Distributor faulty 3. Spark plugs defective 4. Defective wiring between components 1. Insufficient fuel 2. Excess fuel (flooding) 3. Defective choke 4. Defective air filter 1. Wrong type 2. Weather too cold 1. Switches defective 2. Transmission not in park or neutral 3. Seat belt problem (1974 cars) 4. Faulty starter motor 5. Starter drive defective 1. Faulty ground connections 2. Terminals loose or corroded 3. Battery weak 1. Broken ring 2. Excess heat 3. Ringed groove damaged, fouled, or loose 1. Cap cracked 2. Electric corroded 3. Improper point gap 4. High point resistance 5. Faulty condenser 6. Shaft frozen 7. Timing off 8. Motor not keyed properly 1. Car out of gas 2. Clogged fuel line 3. Leaks in fuel line 4. Dirt in fuel tank 5. Fuel line frozen 6. Improperly seated valves 7. Defective fuel pump 8. Cracked carburetor bowl 9. Intake manifold gasket loose 1. Ignition switch 2. Starter relay 3. Neutral start switch 4. Solenoid 1. Paint 2. Corrosion 3. Dirt 4. Loose connections 1. Leaking head gasket 2. Cracked cylinder head 3. Valve burnt, improperly adjusted, or sticking 4. Piston, piston rings, or cylinder worn or broken 5. Gas washdown on cylinders 1. Gap incorrect or fouled 2. Plug shorting 3. Loose or defective wiring 4. Plugs firing in wrong order 1. Belts not fastened 2. Driver's belt switch defective 3. Heavy object on front seat with belt unfastened 4. Belt fastened before driver sits down 1. Fuel pump pressure too high 2. Leaking inlet valve 3. Float out of adjustment 4. Excess pumping of accelerator 5. Excess fuel pressure on hot day 6. Electric fuel pump floods carburetor (foreign cars) 1. Lights left on, motor off 2. Age 3. Cold weather 4. Defective generator 5. Loose or broken fanbelt 6. Electrolyte fluid low or improper 7. Cable wires broken 8. Alternator defective 9. Voltage regulator defective 10. Internal short circuit 11. Too many electric accessories operating 12. Electric leakage 13. Continuous small drain (package on front seat of 1974 models) 14. Battery too small 1. Choke valve open 2. Valve linkage sticks 3. Failure to choke 4. Electric choke misfunction (Volkswagen) Figure 6.10 – A possible fault tree for discovering why a car will not start. (Adapted from Fischhoff et al., 1978 by permission of the authors)
Eliciting decision tree representations 159 other main sub-events. In a subsequent experiment the importance of ‘all other problems’ was emphasized: In particular we would like you to consider its [the fault tree’s] completeness. That is, what proportion of the possible reason for a car not starting are left out, to be included in the category, ‘all other problems’? However, focusing subjects’ attention on what was missing only partially improved their awareness. Fischhoff labeled this insensitivity to the incompleteness of the fault tree ‘out of sight, out of mind’. The finding was confirmed with technical experts and garage mechanics. Neither self-rated degree of knowledge nor actual garage experience has any significant association with subjects’ ability to detect what was missing from the fault tree. Another finding from the study was that the perceived importance of a particular sub-event or branch of the fault tree was increased by presenting it in pieces (i.e. as two separate branches). The implications of this result are far reaching. Decision trees constructed early in the analyst/decision maker interaction may be incomplete representations of the decision problem facing the decision maker. Eliciting decision tree representations What methods have been developed to help elicit decision tree representations from decision makers? One major method, much favored by some decision analysts, is that of influence diagrams 18 which are designed to summarize the dependencies that are seen to exist among events and acts within a decision. Such dependencies may be mediated by the flow of time, as we saw in our examples of decision trees. As we shall see, a close relationship exists between influence diagrams and the more familiar decision trees. Indeed, given certain conditions, influence diagrams can be converted to trees. The advantage of starting with influence diagrams is that their graphic representation is more appealing to the intuition of decision makers who may be unfamiliar with decision technologies. In addition, influence diagrams are more easily revised and altered as the decision maker iterates with the decision analyst. Decision trees, because of their strict temporal ordering of acts and events, need completely respecifying when additional acts and events are inserted into preliminary representations. We shall illustrate the applicability of
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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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Page 124 and 125: 108 Decision making under uncertain
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Page 161 and 162: Constructing a decision tree 145 be
Page 163 and 164: Determining the optimal policy 147
Page 165 and 166: Decision trees and utility 149 succ
Page 167 and 168: Decision trees involving continuous
Page 169 and 170: Practical applications of decision
Page 171 and 172: Assessment of decision structure 15
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Page 177 and 178: Eliciting decision tree representat
Page 179 and 180: Summary 163 where the resulting dec
Page 181 and 182: Exercises 165 other elements of the
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Page 187 and 188: Exercises 171 failing to meet the p
Page 189 and 190: Exercises 173 why it was rational f
Page 191 and 192: Exercises 175 (11) In the Eagle Mou
Page 193 and 194: References 177 ABC Chemicals are pl
Page 195 and 196: 7 Applying simulation to decision p
Page 197 and 198: Monte Carlo simulation 181 Of cours
Page 199 and 200: Monte Carlo simulation 183 Table 7.
Page 201 and 202: Applying simulation to a decision p
Page 213 and 214: Applying simulation to investment d
Page 221 and 222: Summary 205 Hull 10 ), some of them
Page 223 and 224: 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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