Efnisyfirlit

• Front Matter
  • Contents
  • Foreword
  • Preface
    • Accompanying website at http://www.wiley.com/college/goodwin/
• 1 Introduction
  • Complex decisions
  • The role of decision analysis
  • Good and bad decisions and outcomes
  • Applications of decision analysis
    • Improved strategic decision making at Du Pont8
    • Structuring decision problems in the International Chernobyl Project9, 10
    • Selecting research projects at a large international pharmaceutical company11
    • Petroleum exploration decisions at the Phillips Petroleum Company12
    • Prioritizing infrastructure-renewal projects at MIT13
    • Supporting the systems-acquisition process for the US military14
    • Prioritizing projects in a busy UK social services department15
    • Selecting a wide area network solution at EXEL Logistics16
    • Planning under a range of futures in a financial services firm
    • Supporting top-level political decision making in Finland17
    • Automating advice-giving in a building society front office
    • Allocating funds between competing aims in a shampoo-manufacturing company18
    • Anticipating the need for doctors and dentists in the English National Health Service19
    • Monitoring early warning signals in the business environment at Nokia and Statoil20
    • The future of electric-drive vehicles in Germany21
  • Overview of the book
  • References
• 2 How people make decisions involving multiple objectives
  • Introduction
  • Heuristics used for decisions involving multiple objectives
    • The recognition heuristic
    • The minimalist strategy2
    • Take the last2
    • The lexicographic strategy8
    • The semi-lexicographic strategy8
    • Elimination by aspects12
    • Sequential decision making: satisficing
    • Reason-based choice
    • Factors affecting which strategies people employ
  • Other characteristics of decision making involving multiple objectives
    • Decoy effects22
    • Choosing by unique attributes
    • Emotion and choice
    • Justifying already-made choices
    • Partitioning the total cost of an item changes preferences
  • Summary
    • Discussion questions
  • References
• 3 Decisions involving multiple objectives: SMART
  • Introduction
  • Basic terminology
    • Objectives and attributes
    • Value and utility
  • An office location problem
  • An overview of the analysis
  • Constructing a value tree
    • Figure 3.1 – A value tree for the office location problem
  • Measuring how well the options perform on each attribute
    • Table 3.1 – Costs associated with the seven offices
    • Direct rating
      • Figure 3.2 – A value scale for office image
      • Table 3.2 – Values and weights for the office location problem
    • Value functions
      • Figure 3.3 – Constructing a value function for office floor area
      • Figure 3.4 – A value function for distance from customers
  • Determining the weights of the attributes
    • Figure 3.5 – Derivation of swing weights. For example, a swing from the worst to the best location for visibility is considered to be 80% as important as a swing from the worst to the best location for closeness to customers
  • Aggregating the benefits using the additive model
  • Trading benefits against costs
    • Figure 3.6 – A plot of benefits against costs for the seven offices
  • Sensitivity analysis
    • Figure 3.7 – Sensitivity analysis for weight placed on image
  • Theoretical considerations
    • The axioms of the method
    • Assumptions made when aggregating values
  • Conflicts between intuitive and analytic results
    • Value-focused thinking
  • Summary
    • Exercises
  • References
• 4 Decisions involving multiple objectives: alternatives to SMART
  • Introduction
  • SMARTER
    • Table 4.1 – ROC weights
  • Even Swaps
  • Even Swaps versus SMART
    • The relative strengths of Even Swaps
    • The relative limitations of Even Swaps
  • The analytic hierarchy process
    • Figure 4.1 – A hierarchy for the packaging machine problem
    • Table 4.2 – Comparing the importance of ‘Costs’ and ‘Quality’
    • Table 4.3 – Comparing the importance of the ‘Quality’ attributes
    • Table 4.4 – Comparing the machines on ‘purchase cost’
    • Figure 4.2 – Weights for the packaging machine problem
  • Performing AHP calculations by hand
    • Table 4.5 – Random indices for checking the consistency of a table
  • The axioms of the AHP
  • The AHP versus SMART
    • The relative strengths of the AHP
    • Criticisms of the AHP
  • MACBETH
  • Summary
    • Exercises
  • References
• 5 Introduction to probability
  • Introduction
  • Outcomes and events
  • Approaches to probability
    • The classical approach
    • The relative frequency approach
    • The subjective approach
  • Mutually exclusive and exhaustive events
    • The addition rule
      • Table 5.1 – The frequency of flooding of a tidal river in April over the last 20 years
  • Complementary events
  • Marginal and conditional probabilities
    • Table 5.2 – Results of a survey of workers in a branch of the chemicals industry
  • Independent and dependent events
  • The multiplication rule
  • Probability trees
    • Figure 5.1 – A probability tree
  • Probability distributions
    • Figure 5.2 – Probability distribution for the number of planes in service by the end of the year
    • Figure 5.3 – Probability distribution for project completion time
    • Figure 5.4 – Cumulative distribution function for project completion time
  • Expected values
  • The axioms of probability theory
  • Summary
    • Exercises
• 6 Decision making under uncertainty
  • Introduction
  • The maximin criterion
    • Table 6.1 – A decision table for the food manufacturer
  • The expected monetary value criterion
    • Table 6.2 – Another decision table for the food manufacturer
    • Figure 6.1 – A sensitivity analysis for the food manufacturer’s problem
  • Limitations of the EMV criterion
    • Table 6.3 – Returns and probabilities for the new component problem
  • Single-attribute utility
    • Figure 6.2 – A decision tree for the conference organizer’s problem
    • Figure 6.3 – The conference organizer’s decision tree with utilities
    • Figure 6.4 – A demonstration of how expected utility reduces the decision to a simple choice between lotteries
  • Interpreting utility functions
    • Figure 6.5 – A utility function for the conference organizer
    • Figure 6.6 – Interpreting the shape of a utility function
  • Utility functions for non-monetary attributes
    • Figure 6.7 – A decision tree for the drug company research department problem
    • Figure 6.8 – A utility function for product development time
  • The axioms of utility
    • Figure 6.9 – The continuity axiom
    • Figure 6.10 – The substitution axiom
    • Figure 6.11 – Demonstration of the substitution axiom
    • Figure 6.12 – Demonstration of the unequal-probability axiom
    • Figure 6.13 – Demonstration of the compound-lottery axiom
  • More on utility elicitation
  • How useful is utility in practice?
    • Figure 6.14 – Allais’s paradox
  • Multi-attribute utility
    • The Decanal Engineering Corporation
      • Figure 6.15 – A decision tree for the project manager’s problem
    • Mutual utility independence
      • Figure 6.16 – Determining utility independence
    • Deriving the multi-attribute utility function
      • Stage 1
        • Figure 6.17 – Utility functions for overrun time and project cost
        • Table 6.4 – The project manager’s utilities for overrun and cost
      • Stage 2
        • Figure 6.18 – Determining k1
        • Figure 6.19 – Determining k2
        • Figure 6.20 – The project manager’s decision tree with utilities
      • Stage 3
        • Figure 6.21 – Checking the consistency of the decision maker’s responses
    • Interpreting multi-attribute utilities
    • Further points on multi-attribute utility
  • Summary
    • Exercises
  • References
• 7 Decision trees and influence diagrams
  • Introduction
  • Constructing a decision tree
    • Figure 7.1 – An initial decision tree for the food-processor problem
    • Figure 7.2 – A new decision tree for the food-processor problem
  • Determining the optimal policy
    • Figure 7.3 – Rolling back the decision tree
  • Decision trees and utility
    • Figure 7.4 – The engineer’s utility function
    • Figure 7.5 – Apply the rollback method to a decision tree involving utilities
  • Decision trees involving continuous probability distributions
    • Figure 7.6 – The extended Pearson–Tukey (EP-T) approximation method
  • Assessment of decision structure
    • Figure 7.7 – One decision-analytic representation of the calculator problem
    • Figure 7.8 – Toward the correct decision-analytic representation of the calculator problem?
    • Figure 7.9 – Phases of a decision analysis
    • Figure 7.10 – A possible fault tree for discovering why a car will not start (adapted from Fischhoff, B., Slovic, P. and Lichtenstein (1978) Fault Trees: Sensitivity of Estimated Failure Probabilities to Problem Representation, Journal of Experimental Psychology: Human Perception and Performance, 4(2), 330–344. Copyright 1978 © American Psychological Association. By permission of the authors)
  • Eliciting decision-tree representations
    • Figure 7.11 – Definitions used in influence diagrams
    • Figure 7.12 – Influence diagram
    • Figure 7.13 – Decision tree derived from influence diagram
  • Summary
    • Exercises
  • References
• 8 Applying simulation to decision problems
  • Introduction
  • Monte Carlo simulation
    • Table 8.1 – Ten simulations of monthly cash flows
    • Table 8.2 – Estimating probabilities from the simulation results
    • Table 8.3 – The effect of the number of simulations on the reliability of the probability estimates
  • Applying simulation to a decision problem
    • The Elite Pottery Company
      • Stage 1: Identify the factors
        • Figure 8.1 – Identifying the factors that will affect the profit earned by the commemorative plate
      • Stage 2: Formulate a model
      • Stage 3: Preliminary sensitivity analysis
        • Table 8.4 – Estimates of lowest, highest and most likely values for the Elite Pottery problem
        • Figure 8.2 – Tornado diagram showing the effect on profit if each factor changes from its lowest to its highest possible value
      • Stage 4: Assess probability distributions
        • Figure 8.3 – Probability distributions for variable costs, sales and fixed costs
      • Stage 5: Perform the simulation
        • Figure 8.4 – Probability distribution for profit earned by the commemorative plate
      • Stage 6: Sensitivity analysis on the results of the simulation
      • Stage 7: Compare alternative course of action
    • Plotting the two distributions
      • Figure 8.5 – A comparison of the profit probability distributions of the commemorative plate and the figurine
    • Determining the option with the highest expected utility
    • Stochastic dominance
      • First-order stochastic dominance
        • Figure 8.6 – First-order stochastic dominance
      • Second-order stochastic dominance
        • Figure 8.7 – Second-order stochastic dominance
        • Figure 8.8 – An example where the test for second-order stochastic dominance is inconclusive
    • The mean–standard deviation approach
      • Figure 8.9 – The mean–standard deviation screening method
      • Figure 8.10 – (a) A normal probability distribution for profit; (b) examples of quadratic utility functions
  • Applying simulation to investment decisions
    • The NPV method
      • Table 8.5 – Calculating the NPVs for the Alpha and Beta machines
    • Using simulation
      • Figure 8.11 – Probability distributions for the Alpha machine (vertical axes represent probability density)
      • Figure 8.12 – Probability distributions for the NPVs of the Alpha and Beta machines
    • Utility and net present value
  • Modeling dependence relationships
  • Summary
    • Exercises
  • References
• 9 Revising judgments in the light of new information
  • Introduction
  • Bayes’ theorem
    • Figure 9.1 – Tree diagram for the components problem
    • Figure 9.2 – Applying Bayes’ theorem to the components problem
    • Example
    • Answer
      • Figure 9.3 – Applying Bayes’ theorem to the equipment operating problem
    • Another example
      • Figure 9.4 – Applying Bayes’ theorem to the sales manager’s problem
  • The effect of new information on the revision of probability judgments
    • Figure 9.5 – The effect of vague prior probabilities and very reliable information
    • Figure 9.6 – The effect of the reliability of new information on the modification of prior probabilities for the gas-exploration problem
  • Applying Bayes’ theorem to a decision problem
    • Figure 9.7 – (a) A decision tree for the retailer’s problem based on prior probabilities; (b) applying Bayes’ theorem to the retailer’s problem; (c) a decision tree for the retailer’s problem using posterior probabilities
  • Assessing the value of new information
    • The expected value of perfect information
      • Figure 9.8 – Determining the expected value of perfect information
      • Table 9.1 – Calculating the expected value of perfect information
    • The expected value of imperfect information
      • Figure 9.9 – Deciding whether or not to buy imperfect information
      • Figure 9.10 – (a) Revising the prior probabilities when the test indicates that the virus is present; (b) revising the prior probabilities when the test indicates that the virus is absent
      • Figure 9.11 – Determining the expected value of imperfect information
  • Summary
    • Exercises
  • Reference
• 10 Heuristics and biases in probability assessment
  • Introduction
  • Test your judgment
  • Heuristics and biases
  • The availability heuristic
    • Biases associated with the availability heuristic
      • 1. When ease of recall is not associated with probability
      • 2. Ease of imagination is not related to probability
        • Test your judgment: answers to questions 1 and 2
      • 3. Illusory correlation
  • The representativeness heuristic
    • Biases associated with the representativeness heuristic
      • 1. Ignoring base-rate frequencies
        • Test your judgment: answer to question 3
      • 2. Expecting sequences of events to appear random
        • Test your judgment: answer to question 4
      • 3. Expecting chance to be self-correcting
        • Test your judgment: answers to questions 5 and 6
      • 4. Ignoring regression to the mean
        • Test your judgment: answer to question 7
      • 5. The conjunction fallacy
        • Test your judgment: answers to questions 8 and 9
  • The anchoring and adjustment heuristic
    • Biases associated with anchoring and adjustment
      • 1. Insufficient adjustment
        • Test your judgment: answer to question 10
      • 2. Overestimating the probability of conjunctive events
        • Test your judgment: answer to question 11
      • 3. Underestimating probabilities for disjunctive events
        • Test your judgment: answer to question 12
      • 3. Overconfidence
        • Test your judgment: answer to question 13
  • Other judgmental biases
    • 1. Believing desirable outcomes are more probable
    • 2. Biased assessment of covariation
      • Test your judgment: answer to question 14
  • Is human probability judgment really so poor?
    • 1. Participants in studies may be unrepresentative of real decision makers
    • 2. Laboratory tasks may be untypical of real-world problems
    • 3. Experimental tasks may be understood in different ways by participants
    • 4. Participants may be poorly motivated
    • 5. Citation bias
    • 6. Real-world studies suggest better performance
    • 7. People think in terms of frequencies not probabilities
      • Figure 10.1 – A methodology for choosing how to develop a subjective probability assessment
      • Exercises
  • References
• 11 Methods for eliciting probabilities
  • Introduction
  • Probability assessment
  • Issues with verbal probability expressions
  • Coherence in probability judgments
  • Two barriers to improving probability assessments through learning
  • Preparing for probability assessment
    • Motivating
    • Structuring
    • Conditioning
  • Assessment methods
    • Assessment methods for individual probabilities
      • Direct assessments
      • The probability wheel
        • Figure 11.1 – A probability wheel
    • Assessment methods for probability distributions
      • The probability method
      • Graph drawing
        • Figure 11.2 – The method of relative heights
  • A comparison of the assessment methods
  • Consistency and coherence checks
  • Assessing the validity of probabilities
    • Figure 11.3 – Calibration curves
  • Assessing probabilities for very rare events
    • Event trees
      • Figure 11.4 – An event tree
    • Fault trees
      • Figure 11.5 – A fault tree
    • Using a log-odds scale
      • Figure 11.6 – A log-odds scale
  • Communicating probability estimates
  • Summary
    • Exercises
  • References
• 12 Risk and uncertainty management
  • Introduction
  • The Two Valleys Company
    • Exploring sources of uncertainty
      • Figure 12.1 – Sources of uncertainty at the Two Valleys Company
      • Table 12.1 – Estimated values for uncertain factors
      • Figure 12.2 – Cumulative probability distributions for annual profit at the two sites
    • Identifying possible areas for uncertainty management
      • 1. Calculate the effect of perfect control
        • Table 12.2 – Results of risk management actions
        • Figure 12.3 – Tornado diagram for Littleton
      • 2. Repeat the above process for the next best option
        • Figure 12.4 – Tornado diagram for Callum Falls
    • Using brainstorming to create actions to improve the preferred policy
      • Table 12.3 – Ideas for risk management at Two Valleys Company
      • Figure 12.5 – The effectiveness of risk management measures
  • Summary
    • Exercise
      • Figure 12.6 – Cumulative probability distribution for the AB Charity
      • Figure 12.7 – Tornado diagram for the AB Charity
  • References
• 13 Decisions involving groups of individuals
  • Introduction
  • Mathematical aggregation
    • Table 13.1 – The production manager’s utilities and probabilities
    • Table 13.2 – The accountant’s utilities and probabilities
    • Table 13.3 – The average of the utilities and probabilities
  • Aggregating judgments in general
    • Taking a simple average of the individual judgments
    • Taking a weighted average of the individual judgments
  • Aggregating probability judgments
    • Table 13.4 – Averaging probabilities
  • Aggregating preference judgments
    • Aggregating preference orderings
    • Aggregating values and utilities
      • Figure 13.1 – Measuring individuals’ strengths of preference against a common scale
  • Unstructured group processes
  • The Delphi method
    • Figure 13.2 – The accuracy of the median estimate
    • The benefits of establishing heterogeneity in Delphi panels
    • Enhancing the evaluation of rationales by devil’s advocacy and dialectical inquiry
  • Prediction markets
  • Decision conferencing
  • Summary
    • Discussion questions
  • References
• 14 Resource allocation and negotiation problems
  • Introduction
  • Modeling resource allocation problems
    • An illustrative problem
  • The main stages of the analysis
    • Determining the areas, resources and benefits5
  • Identifying the possible strategies for each region
    • Figure 14.1 – Possible strategies identified by managers of the furniture company
    • Assessing the costs and benefits of each strategy
      • Table 14.1 – Values of the strategies in the individual regions
    • Measuring each benefit on a common scale
      • Figure 14.2 – The within-criterion weights for the furniture company problem
      • Table 14.2 – Values of strategies with each benefit measured on a common scale
    • Comparing the relative importance of the benefits
      • Figure 14.3 – The across-criteria weights for the furniture company problem
    • Identifying the costs and benefits of the packages
      • Figure 14.4 – Identifying the efficient frontier for the furniture company problem
      • Figure 14.5 – Investigating the costs and benefits of strategies in the individual regions
  • Sensitivity analysis
  • Negotiation models
  • An illustrative problem
    • Figure 14.6 – Management and union value functions and weights
    • Table 14.3 – Calculation of values for the tentative management–union deal
    • Figure 14.7 – Identifying the efficient frontier for the management–union negotiations
  • Practical applications
  • Summary
    • Discussion questions
      • Table 14.4 – Details of deals
      • Figure 14.8 – Values of deals
  • References
• 15 Decision framing and cognitive inertia
  • Introduction
  • Creativity in problem solving
    • Figure 15.1 – Moves to attain the goal state in the first water-jug problem
    • Figure 15.2 – Moves to attain the goal state in the second water-jug problem
    • Figure 15.3 – The nine-dot problem
  • How people frame decisions
  • Solving the wrong problem
  • Get hooked on complexity – overlooking simple options
  • Imposing imaginary constraints and false assumptions on the range of options
  • Sensitivity to reference points
    • Prospect theory
      • Figure 15.4 – Value function in prospect theory
      • Figure 15.5 – Decision weights in prospect theory
      • Figure 15.6 – Choice between alternatives P and Q
      • Figure 15.7 – P and Q with values and decision weights replacing original numbers
  • Mental accounting and the narrow bracketing of decisions
  • Inertia in strategic decision making
    • Real-world studies
    • Studies in the psychological laboratory
  • Non-rational escalation of commitment
  • How people react to a threat
  • Rethinking decisions
    • Figure 15.8 – An example of a response to Russo and Schoemaker’s frame analysis questions
  • Studies in the psychological laboratory and cognitive inertia: a synthesis
    • Figure 15.9 – The relationship between the perceived business environment and the strategic decision process
  • Summary
    • Discussion questions
  • Appendix
    • Figure 15.10 – Solution to the nine-dot problem (Figure 15.3)
  • References
• 16 Scenario planning: an alternative way of dealing with uncertainty
  • Introduction
  • Figure 16.1 – The Müller–Lyer illusion
  • Scenario construction: the extreme-world method
    • Figure 16.2 – Steps in scenario construction: the extreme-world method
    • Figure 16.3 – Predetermined trends
    • Figure 16.4 – Key uncertainties
    • Figure 16.5 – Three scenarios
  • Using scenarios in decision making
    • Figure 16.6 – An illustrative business idea for a business school
    • Figure 16.7 – Testing the robustness of strategies against scenarios
    • Figure 16.8 – A ‘real’ scenario of future trading patterns
  • Scenario construction: the driving forces method
    • Figure 16.9 – An output of a ‘driving forces’ scenario structuring methodology
    • Figure 16.10 – Adam Kahane’s four South African scenarios
    • Figure 16.11 – Steps in scenario construction: the driving force method
    • Figure 16.12 – Stakeholder structuring space
  • Case study of a scenario intervention in the English National Health Service6
  • Case study of a scenario intervention in the public sector7
    • Forward to the past
    • Free enterprise
    • People’s kailyard8
    • Technology serves
      • Table 16.1 – The components of the futures methodologies
  • Issues and limitations in scenario planning
    • Organizational context and scenario planning
    • Case study of an unsuccessful scenario-planning intervention
    • Dealing with low predictability
  • Conclusion
    • Discussion questions
  • References
• 17 Combining scenario planning with decision analysis
  • Introduction
  • Main stages of the approach
    • Table 17.1 – Stages in the application of SMART to scenario planning
  • Illustrative case study
    • Stage 1: Formulate objectives
    • Stage 2: Formulate scenarios
    • Stage 3: Design alternative strategies
    • Stage 4
      • Table 17.2 – Strategy ranks across different scenarios for each objective (1 = best)
      • Table 17.3 – Scores for strategies under the different scenarios for each objective
    • Stage 5
      • Table 17.4 – Ranking of swings
      • Table 17.5 – Obtaining the weights for the objectives
    • Stage 6: For each strategy/scenario combination use the performance scores and weights to determine a weighted aggregate score
      • Table 17.6 – Obtaining an aggregate score for Status Quo in the Mail Mountain scenario
    • Stage 7: Produce a table of strategy/scenario aggregate scores and use this to assess and compare the strategies’ performances, paying particular attention to the robustness of performance over the range of scenarios
      • Table 17.7 – Aggregate scores
    • Stage 8: Perform sensitivity analysis
      • Table 17.8 – The effect of changing the weight on growth
  • Extensions and alternatives to the above method
    • Having a checklist of key objectives
    • Making the ranking process easier
    • Avoiding the need to assign scores and weights
    • Different weights for different scenarios
    • Other approaches
  • Summary
    • Exercises
  • References
• 18 Alternative decision-support systems and conclusions
  • Introduction
  • Expert systems
    • What is an expert system?
    • What is expert knowledge?
  • How is expert knowledge represented in expert systems?
    • Marketing applications
    • Financial services applications
      • Figure 18.1 – Underwriting options
      • Figure 18.2 – The rule base of the geographical module
    • Where next?
    • Back-office fraud-detection systems
    • Point-of-sale advice-giving systems
    • Conclusion
  • Statistical models of judgment
    • If linear models are so effective, why aren’t they more prevalent in practice?
    • Comparisons of decision-aiding techniques
  • Snap decisions and decision analysis: why not trust our initial intuitions?
  • Designing decisions so that people make the ‘best’ choice
  • Some final words of advice
    • Are my assumptions about the business environment and the future valid?
    • Who should I involve in the decision?
    • Have I made an adequate search for alternative courses of action?
    • How much effort is the decision worth? Which aspects of the problem require the most effort?
    • Which method(s) are likely to help with my decision problem?
      • Table 18.1 – Summary of techniques covered in the book
  • Summary
  • References
• Back Matter
  • Suggested answers to selected questions
    • Chapter 2
    • Chapter 3
    • Chapter 4
    • Chapter 5
    • Chapter 6
    • Chapter 7
    • Chapter 8
    • Chapter 9
    • Chapter 11
    • Chapter 14
    • Chapter 17
  • Index

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