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Decision Making

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Title: Decision Making


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
Simons 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 rivers 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
R
L
10
States and Operators
  • State ltFarmer/Boat location, Wolf location,
  • Goat location, Cabbage locationgt
  • Operator
  • ltL,L,L,Lgt ----gt ltR,R,L,Rgt
  • ..

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

13
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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

16
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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-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. 1974. Judgement
    Under Uncertainty Heuristics and Biases.
    Science, 1851124-31

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
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