Decision Making

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Decision Making
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Outline

• Definitions
• Decisions and alternatives
• Characterizing decisions
• Decision making strategies
• Decision making phases
• Implications for decision support

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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

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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.

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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?

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Phase I Intelligence

• Problem Identification and Definition
• What's the problem?
• Why is it a problem?
• Whose problem is it?

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Phase II Design

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

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Problem Solving

• State Space Search
• Initial State
• Goal State
• Operators
• Choosing representation and controlling the
  application of operators requires decision making

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Problem Representation
R
L
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States and Operators

• State ltFarmer/Boat location, Wolf location,
• Goat location, Cabbage locationgt
• Operator
• ltL,L,L,Lgt ----gt ltR,R,L,Rgt
• ..

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Phase III Choice

• Solution
• Determine the outcome of chosen alternatives
• Select the/an outcome consistent with the
  decision strategy

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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

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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

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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

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Decision Making Strategies

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

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Decision Making Strategies

• Considerations
• Individual-focused vs. organization-focused
  decisions
• Individual vs. group decisions
• Expensive-to-change vs. inexpensive-to-change
  decisions

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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

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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

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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

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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

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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

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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

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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

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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

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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?

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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?

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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

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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?

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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?

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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

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Example

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

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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?

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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?)

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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

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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.

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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

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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

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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?

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Value of DSS

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