1. INFM 718A / LBSC 705 Information For Decision Making
Lecture 10

2. Outline Multi-attribute Utility (MAU) models In-class MAU exercise
Preview of Assignment 6

3. “Soft” Decision Methods
Some decision situations can not be addressed with the “hard” decision techniques we worked on so far.
We approach such decision situations using “softer” techniques, which provide margin for subjective judgments.

4. Multi-attribute Utility (MAU) Models
Furthermore, some decisions involve more than criterion.
There are both hard and soft methods that address multi-objective decision problems.
Multi-attribute Utility Model approach is at the intersection of “soft” and “multi-objective” decision making.

5. MAU-type Decision Situations
The decision depends on more than one criterion. You cannot just optimize a single criterion, and get a “solution.”
Some or all criteria are “soft,” or subjective. (There can be objective criteria, as well.)
Criteria may have different importance levels, or weights.

6. An Example Situation Buying a car.
Three options: Yugo, Hyundai, Mercury
Three criteria: Price, reliability, safety
Criteria have different weights.

7. MAU Procedure You are trying to add apples and oranges.
Assign scores (some are already given objectively.)
Assign weights
Normalize weights
Normalize scores
Calculate overall scores, and rank options

8. Normalizing Weights
The total of the normalized (adjusted) weights should be 1.

9. Normalizing Scores “Good” criteria (“the higher the better”)
“Bad” criteria (“the lower the better”)

10. Adjusting Weights
Relative weight of a criterion depends mainly on two things:
How important the criteria is for the decision,
How different the best and the worst choices based on that criterion.
If the difference based on that criterion between the best and worst choice is not big, the relative weight should be lower, even when the criterion is important.

11. Sensitivity Analysis What if the scores or weights were different?
Would the ranking, or the relative “strengths” of the options be different?
What amount of change in scores or weights would yield a different decision?

12. Sensitivity Analysis
Change the score and weight values one by one, or in combinations to see what happens.
An exhaustive coverage of all possible cases may be impractical.
However, some pattern, and valuable insights emerge relatively quickly.

13. Let’s Work on the Car Example
Price
Reliability
Safety
Yugo
$ 6,000
Hyundai
$ 8,600 10 4
Mercury
$ 10,000 6
Price
Reliability
Safety
Weights 20 25 15