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Decision Making Decision Making Outline • Definitions •

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Decision Making Decision Making Outline • Definitions • Powered By Docstoc
					Decision Making
                    Outline

•   Definitions
•   Decisions and alternatives
•   Characterizing decisions
•   Decision making strategies
•   Decision making phases
•   Implications for decision support
                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
      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.
               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?
       Phase I: Intelligence


– Problem Identification and Definition
   • What's the problem?
   • Why is it a problem?
   • Whose problem is it?
         Phase II: Design

– Problem Structuring
   • Generate alternatives
   • Set criteria and objectives
   • Develop models and scenarios to
     evaluate alternatives
   • Solve models to evaluate alternatives
          Problem Solving

• State Space Search
  – Initial State
  – Goal State
  – Operators

• Choosing representation and controlling the
  application of operators requires decision
  making
Problem Representation

L                 R
        States and Operators
• State = <Farmer/Boat location, Wolf location,
Goat location, Cabbage location>

• Operator
• <L,L,L,L> ----> <R,R,L,R>
• …..
        Phase III: Choice


– Solution
   • Determine the outcome of chosen
     alternatives
   • Select the/an outcome consistent with the
     decision strategy
 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
   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
   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
   Decision Making Strategies
• Strategies:
  – Optimizing
  – Satisficing
  – Quasi-satisficing
  – Sole decision rule
  – Selection by elimination
  – Incrementalism and muddling through
   Decision Making Strategies
• Considerations
  – Individual-focused vs. organization-focused
    decisions
  – Individual vs. group decisions
  – Expensive-to-change vs. inexpensive-to-
    change decisions
               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]
                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
                   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
           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
      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
    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
           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
         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
                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?
                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?
               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
               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?
                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?
    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
                 Example

What is the most likely sequence of gender
 for series of children born within a family?
- The sequence of BBGGBG, BGBBBG,
 BBBBGG?
                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?
        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?)
                 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
  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.
          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
 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
 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?
              Value of DSS
• Increase the bounds of rationality
  – easier access to information
  – identify relevant information
  – increase ability to process information

				
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