1. Decision Making

2. Outline Definitions Decisions and alternatives
Characterizing decisions
Decision making strategies
Decision making phases
Implications for decision support

3. Definitions Choice about a “course of action” -- Simon
Choice leading to “a certain desired objective”
-- Churchman
Knowledge indicating the nature of a commitment to action
-- Holsapple and Whinston

4. Simon’s Model of Problem Solving
Decision-making consists of three major phases---intelligence, design, and choice [Simon]
H.A. Simon The New Science of Management Decision. Harper and Row, NY.
Newell, A., & Simon, H.A. (1972). Human Problem Solving. Prentice-Hall, Englewood Cliffs, NJ.

5. Example
A farmer with his wolf, goat, and cabbage come to the edge of a river they wish to cross. There is a boat at the river’s edge, but of course, only the farmer can row. The boat can only handle one animal/item in addition to the farmer. If the wolf is ever left alone with the goat, the wolf will eat the goat. If the goat is left alone with the cabbage, the goat will eat the cabbage. What should the farmer do to get across the river with all his possessions?

6. Phase I: Intelligence Problem Identification and Definition
What's the problem?
Why is it a problem?
Whose problem is it?

7. Phase II: Design Problem Structuring Generate alternatives
Set criteria and objectives
Develop models and scenarios to evaluate alternatives
Solve models to evaluate alternatives

8. Problem Solving State Space Search Initial State Goal State Operators
Choosing representation and controlling the application of operators requires decision making

9. Problem Representation

10. States and Operators State = <Farmer/Boat location, Wolf location,
Goat location, Cabbage location>
Operator
<L,L,L,L> ----> <R,R,L,R>
…..

11. Phase III: Choice Solution
Determine the outcome of chosen alternatives
Select the/an outcome consistent with the decision strategy

12. Decisions and Alternatives
where do they come from?
how many are enough?
Evaluation
how should each alternative be evaluated?
how reliable is our expectation about the impact of an alternative?
Choice
What strategy will be used to arrive at a choice?
E.g., DxPlain

14. Human Cognitive Limitations (Harrison, 1995)
Retain only limited information in short-term memory
Display different types and degrees of intelligence
Those who embrace closed belief systems restrict information search
Propensity for risk varies
Level of aspiration positively correlated to desire for information

15. Common Perceptual Blocks (Clemen, 1991)
Difficulty in isolating a problem
Delimiting the problem space too closely
Inability to see the problem from various perspectives
Stereotyping
Cognitive saturation or overload

17. Decision Making Strategies
Optimizing
Satisficing
Quasi-satisficing
Sole decision rule
Selection by elimination
Incrementalism and muddling through
Consider some well-known decision problems
- Health care reform
- Faculty hiring process

18. Decision Making Strategies
Considerations
Individual-focused vs. organization-focused decisions
Individual vs. group decisions
Expensive-to-change vs. inexpensive-to-change decisions

19. Optimizing Goal: select the course of action with the highest payoff
estimation of costs and benefits of every viable course of action
simultaneous or joint comparison of costs and benefits of all alternatives
high information processing load on humans
people do not have the ``wits to maximize'' [Simon]

20. Observations Given high cost in time, effort, and money
Decisions are made under severe time pressure (``fighting fires'')
Optimization on stated objectives may result in sub-optimization on unstated, less tangible objectives
Therefore, people often
Do not consider all alternatives
Do not evaluate all alternatives thoroughly and rigorously
Do not consider all objectives and criteria
Place more weight on intangible objectives and criteria

21. Satisficing
Decision-makers satisfice rather than maximize [Simon]. They choose courses of action that are ``good enough''---that meet a certain minimal set of requirements
Theory of bounded rationality: human beings have limited information processing capabilities
Optimization may not be practical, particularly in a multi-objective problem, yet knowing the optimal solution for each objective and under various scenarios can provide insight to make a good satisficing choice

22. Sole Decision Rule
``Tell a qualified expert about your problem and do whatever he (she) says---that will be good enough'' [Janis and Mann]
Rely upon a single formula as the sole decision rule
Use only one criterion for a suitable choice
e.g., do nothing that may be good for the enemy
Impulsive decision-making usually falls under this category

23. Selection by Elimination
Eliminate alternatives that do not meet the most important criterion (screening; elimination by aspects)
Repeat process for the next important criterion, and so on
Decision-making becomes a sequential narrowing down process

24. Selection by Elimination
``Better'' alternatives might be eliminated early on---improper weights assigned to criteria
Decision-maker might run out of alternatives
For complex problems, this process might still leave decision maker with large number of alternatives

25. Incrementalism
Often, decision-makers have no real awareness of arriving at a new policy or decision
decision-making is an ongoing process
the satisficing criteria themselves might change over time
Make incremental improvements over current situation and aim to reach an optimal situation over time
Useful for ``fire-fighting'' situations
Frequently found in pluralistic societies and organizations

26. Heuristics and Biases
Heuristics are “rules of thumb” that can make a search process more efficient.
Most common biases in the use of heuristics
Availability
Adjustment and anchoring
Representativeness
Motivational
A. Tversky and D. Kahneman “Judgement Under Uncertainty: Heuristics and Biases.” Science, 185:

27. Example 1 Which is riskier (probability of serious accident):
a. Driving a car on a 400 mile trip?
b. Flying on a 400 mile commercial airline flight?

28. Example 2 Are there more words in the English language
a. that start with the letter r ?
b. for which r is the third letter?

29. Availability “what is easily recalled must be more likely”
Inability to accurately assess the probability of a particular event happening
Assess based on past experience which may not be representative
Structured review and analysis of objective data can reduce availability bias

30. Example 1
A newly hired programmer for a software firm in Pittsburgh has two years experience and good qualifications. When an employee at Au Bon Pain was asked to estimate the starting salary she guessed $40,000. What is your estimate?
a. $30,000 - $50,000?
b. $50,000 - $70,000?
c. $70,000 - $90,000?

31. Example 2
A newly hired programmer for a software firm in Pittsburgh has two years experience and good qualifications. When an employee at Au Bon Pain was asked to estimate the starting salary she guessed $80,000. What is your estimate?
a. $30,000 - $50,000?
b. $50,000 - $70,000?
c. $70,000 - $90,000?

32. Adjustment and Anchoring
Make estimates by choosing an initial value and then adjusting this starting point up or down until a final estimate is obtained
Most subjectively derived probability distributions are too narrow and fail to estimate the true variance of the event
Assess a set of values, instead of just the mean

33. Example
What is the most likely sequence of gender for series of children born within a family?
- The sequence of BBGGBG, BGBBBG, BBBBGG?

34. Example
Mike is finishing his CMU MMM degree. He is very interested in the arts and at one time considered a career as a musician. Is Mark more likely to take a job:
a. In the management of the arts?
b. A medical management position?

35. Representativeness
Attempt to ascertain the probability that a person or object belongs to a particular group or class by the degree to which characteristics of that person or object conform to a stereotypical perception of members of that group or class. The closer the similarity between the two, the higher is the estimated probability of association
Small sample size bias
Failure to recognize regression to the mean (predicted outcomes representative of the input?)

36. Motivational
Incentives, real or perceived, often lead to probability estimates that do not accurately reflect his or her true beliefs
Non-cognitive, motivational biases
Difficult to address through the design of a DSS
Solicit a number of estimates from similar sources, both related and unrelated to problem context

37. Summary: Heuristics and Biases
Heuristics are rules of thumb that we use to simplify decision making.
Overall, heuristics result in good decisions. On average any loss in quality of decision is outweighed by the time saved.
But, heuristics can cause biases and systematic errors in decision making when they fail.
In addition, we are typically unaware of the heuristics and biases, and fail to distinguish between situations in which their use is more and less appropriate.

38. Evaluation Metrics Effectiveness: what should be done
Easier access to relevant information
Faster, more efficient problem recognition and identification
Easier access to computing tools and models
Greater ability to generate and evaluate large set of alternatives
Efficiency: how should it be done
Reduction in decision costs
Reduction in decision time for same level of detail in the analysis
Better quality feedback

39. Implications for Decision Support
Different people will use different strategies at different times for different kinds of decisions
Which decision strategy to engineer in a decision support system?
Multiple strategies may be used in making a decision

40. Implications for Decision Support
Is there an ``optimal decision strategy’’ for each problem?
What are the information processing requirements for each decision-making strategy?
Which strategy do decision-makers favor, When, and Why?

41. Value of DSS easier access to information
Increase the bounds of rationality
easier access to information
identify relevant information
increase ability to process information