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

2. Introduction Module 3: Module 4: Module 4 software tutorial
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 Choices Learning 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 Choices Learning 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