Simon's model of problem solving consists of three phases - intelligence, design, and choice. In the intelligence phase, the problem is identified and defined. In the design phase, alternatives are generated and evaluated. In the choice phase, the best alternative is selected. Decision making strategies include optimizing, satisficing, and incrementalism. Heuristics can introduce biases like availability, adjustment and anchoring, and representativeness. Decision support systems aim to increase rationality by improving access to relevant information and the ability to evaluate alternatives.

1. Decision Making

2. Outline
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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. 1960. 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
L
R

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
• 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
• Strategies:
– Optimizing
– Satisficing
– Quasi-satisficing
– Sole decision rule
– Selection by elimination
– Incrementalism and muddling through

18. Decision Making Strategies
• Considerations
– Individual-focused vs. organization-focused
decisions
– Individual vs. group decisions
– Expensive-to-change vs. inexpensive-tochange 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. 1974. “Judgement Under
Uncertainty: Heuristics and Biases.” Science, 185:1124-31

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
• Increase the bounds of rationality
– easier access to information
– identify relevant information
– increase ability to process information