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Module 4 Modeling Decisions: MAKING CHOICES

Module 4 Modeling Decisions: MAKING CHOICES. Topics: Creating case study decision tree Solving a decision tree Risk profiles Dominance of alternatives Attributes and scales Using multiple objectives. Introduction. Module 3: Structure values and objectives Identify performance measures

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Module 4 Modeling Decisions: MAKING CHOICES

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  1. Module 4 Modeling Decisions: MAKING CHOICES Topics: Creating case study decision tree Solving a decision tree Risk profiles Dominance of alternatives Attributes and scales Using multiple objectives

  2. Introduction • Module 3: • Structure values and objectives • Identify performance measures • Structure decision tree and influence diagram models • Module 4: • Solve decision trees • Approach for multiple objectives • Module 4 software tutorial

  3. Making ChoicesLearning Objectives • Create decision tree from case study • Solve a decision tree • Expected value preference criterion • Create and interpret • Risk profiles • Cumulative risk profiles • Concept of dominance • Definition and identification • Decision problem simplification

  4. Making ChoicesLearning Objectives • Develop • Constructed attributes • Constructed scales • Formulate multiple objectives problems • Common scales • Trade–off weights • Composite consequences

  5. Making Choices • Analysis of structured problems • graphing • calculating • examining results

  6. “Texaco versus Pennzoil” • Pennzoil and Getty Oil agreed to a merger • Texaco made better offer to Getty • Getty reneged on Pennzoil and sold to Texaco • Pennzoil sued Texaco for interference • Pennzoil won and was awarded the $11.1 billion

  7. “Texaco versus Pennzoil” • Texaco appealed; award reduced to $10.3 billion • Texaco threatened bankruptcy if Pennzoil filed liens • Texaco also threatened to take case to Supreme Court

  8. “Texaco versus Pennzoil” • Texaco offered to settle out of court by paying Pennzoil $2 billion • Pennzoil believed fair settlement between $3 and $5 billion

  9. “Texaco versus Pennzoil” • What should Pennzoil do? • Accept $2 billion settlement • Make counteroffer • Assume objective is to maximize settlement

  10. Decision Trees and Expected Monetary Value • Expected Monetary Value (EMV); i.e., select alternative with highest expected value • “Folding back the tree” or “rolling back” procedure

  11. Decision Trees and Expected Monetary Value Folding Back: • Start at the endpoints of the branches on the far right-hand-side and move to the left • Calculate expected values at a chance node • Choose the branch with the highest value or expected value at a decision node.

  12. Expected Monetary Value • Weighted average of outcomes at chance node • Sum of the product of each outcome and its probability

  13. Pennzoil’s Decision Tree • Pennzoil’s final decision tree figure 4.7 • What has been decided? • Pennzoil should reject Texaco’s offer and make a $5 billion counteroffer • If Texaco then makes a $3 billion counteroffer, Pennzoil should take its chances in court

  14. Solving Influence Diagrams • More cumbersome than decision trees • Conversion to symmetric decision tree • Software packages used

  15. Risk Profiles • Graph illustrating chances of possible payoffs or consequences • One profile for each strategy graph 4.18

  16. Risk Profiles • Creation is straightforward process, but tedious • Can create for strategies and specific sequences • Only strategies for first one or two decisions examined

  17. Risk Profiles • Three steps to follow: • Determine probabilities of paths • Determine probabilities of payoffs • Create charts for strategies

  18. Dominance • Dominating alternative always preferred over another alternative • Dominating alternative always has higher EV than other alternative

  19. Dominance • May enable elimination of alternatives early in the process • Elimination simplifies and reduces cost of the process

  20. Dominance Approaches: • Inspection • Cumulative distribution function • Cumulative risk profile • Sensitivity analysis • Tornado diagram

  21. Attributes and Scales Measurement of fundamental objectives • Measurement crucial to evaluation of consequences • Methods must be consistent with objectives • Attributes and attribute scales define measurement • Different types of attributes

  22. Attributes and Scales • Purpose: Explore attributes and scales that measure achievement of objectives • Major field of study and in-depth exploration beyond scope of cource

  23. Attributes and Scales • Attribute: measure of performance or merit • Scale: defined graduated series or specified scheme • Scale frequently implicit in attribute definition

  24. Types of Attributes Keeney identifies three types of attributes: • Natural attributes • generally known and have common meaning • for example, centimeters • Constructed attributes • created when no natural attributes exists • for example, qualitative ratings • Proxy attributes • indirect measures (either natural or constructed) when no direct measures exist • for example, use “sulphur dioxide concentration” for “acid rain damage to sculptures”

  25. Constructed Attributes • Intellectually challenging and demanding • Requires depth of knowledge and understanding of decision situation and objectives • Three properties • measurable: define objective in detail • operational: describe possible consequences • understandable: no ambiguity

  26. Constructed Attributes • Frequently needed and most challenging • A constructed attribute of site biological impact

  27. Constructed Attributes • Implied scale may not reflect measures needed • Nominal values in rank order may not correspond to rational scale • For example (level 2 – level 1) ?≠? (level 4 – level 3) • Use subjective judgment to rate nominal values on rational scale

  28. Constructed Attributes • Define constructed attributes from natural attributes • Need to compare or combine constructed and natural attributes • Convert natural attributes to constructed scale using proportions

  29. Multiple Objectives Problems require: • Common scale for measurement of consequences • Trade–off weights for objectives • Single composite consequence

  30. Multiple Objectives Common scale for consequences: • Select common scale • May be one used for an objective • May be one not already used • May be natural or constructed • Tendency toward constructed with utility values • Convert consequence measures for each objective to common scale

  31. Multiple Objectives Trade–offs weights: • Value between zero and one • Sum to unity • Consider consequence range • Reflect relative importance of objectives • Consistent with objectives hierarchy

  32. Multiple Objectives Composite consequence for final outcomes: • Linear combination of individual consequences • Trade–off weights are coefficients

  33. Summary • Creation of decision tree from case study • Solution of case study decision tree • Construction and use of risk profiles • Definition and use of dominance • Attributes and attribute scales, particularly constructed attributes • Formulation and solution of a multiple objectives problem

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