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Decision Making

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					Decision Making
 Human Factors Psychology
        Dr. Steve
Historically Bad Decisions


 •   Challenger explodes when launched in cold weather
 •   U.S.S. Vincennes shoots down Iranian civilian jetliner
 •   U.S. military is ambushed at Bay of Pigs
 •   Red Sox owner sells Babe Ruth to the Yankees
 •   Sony sticks with Betamax format for video recording
 •   U.S. supports Mujahadeen in war with Russia
Definitions
   Decision making – selecting one choice from a
    number of choices involving some level of uncertainty.

   Intuitive Decision Making – quick and relatively
    automatic responses to a problem.
       Ex: Response to yellow traffic light


   Analytic Decision Making – slow, deliberate, and
    controlled responses to a problem.
       Ex: What stock to purchase
Rational Decision Making
Research
    Normative Decision Models – Assumes individuals
     act rationally in trying to find the best solution to
     optimize outcome.
        Utility – overall value or worth of a choice
        Expected Value – is the overall value of the choice
         determined by multiplying the utility of the choice times the
         probability of the outcome
             Ex: The decision to buy a lottery ticket may be determined by
              how much the jackpot is worth times the probability of winning
Subjective Expected Utility
Theory
  Alternative/Outcomes          Probability       Utility      PxU       Alternative
                                  (0 to 1)     (-10 to +10)              Expected
                                                                            Utility
Use Safety Device                                                                 -1.7
   Accident: Injury or death             .10          +10.0      +1.0
is prevented
  No Accident: inefficient,              .90            -3.0      -2.7
restrictive, discomfort
Not Use Safety Device                                                             +5.3
  Accident: Serious injury or            .10          -10.0       -1.0
death
   No Accident: more                     .90           +7.0      +6.3
efficient and comfortable
                                Model predicts workers will not use the safety device
                                What if probability of an accident was .50?
Descriptive Decision Models
   Descriptive Decision Models – Assume humans do not
    act rationally in decision making (assumptions of normative
    models are violated)
       Framing Effects – The way a problem is phrased affects the
        decision
            Ex: Choice to have surgery affected by whether told you have a 60%
             chance of living, or told you have a 40% chance of dying.
       Satisficing – making a decision that is just good enough without
        taking extra time and effort to do better
            Ex: Decision as to how well I should define a term
Algorithms vs. Heuristics
             Solve the anagram: metssy

    Algorithms are procedures that will always lead to
     correct answer
        Ex: Try every combination – metssy mtssye mssyet msyets
         ….
    Heuristics are shortcuts that are not guaranteed to
     lead to best answer, but are more efficient
        Ex: use rules of thumb such as need a vowel to separate
         consonant sounds –mestys stymes system
  Information Processing Model
  applied to Decision Making
Cue Reception/Integration
-cues from environment are placed
in working memory (cues possess
uncertainty)

Hypothesis Generation
- guesses about cues are made
drawing form LTM

Hypothesis Evaluation/Selection
- collect additional cues to test the
hypothesis

Generating/Selecting Actions
- alternative actions are generated
by retrieving possibilities from LTM
Factors influencing
Decision Making
   Amount of cue info brought into Working Memory
       Function of attentional demands
   Time available for decision-making
       Function of the time criticality of task
   Attentional resources
       Function of how many tasks are occurring concurrently
   LTM retrieval ability
       Person may possess right info, but fail to retrieve it (inert knowledge)
   Working Memory Capacity
       Can only hold so much info in WM at one time

How do these factors affect your use of automated phone menu systems?
Criteria for “Good” Decisions
    Outcome produces maximum value
        Problem is that decision are often made to avoid worst
         outcome rather than maximize value
             Ex: Decision to buy the extended warranty on an appliance
    Positive vs. Negative outcome
        Problem is decision may be positive in the short term, but
         turn out to be a big mistake later
             Ex: Japan’s decision to attack U.S. in 1941
    Comparison to expert’s decisions
        Problem is that experts don’t always make good decisions
             Ex: Experts’ decision to launch Challenger
Heuristics
Biases in using cues for DM
    Attention to a limited number of cues – affected by
     the magical number 7
    Cue Primacy – the first few cues are given greater
     importance
    Anchoring Heuristic – once an initial decision is
     made, later cues are often ignored
    Cue salience – cues that are easily noticed are most
     likely to be used
    Overweighting of unreliable cues – reliability of cues
     is often overlooked
Heuristics
Biases in hypothesis generation
   Limited number of hypotheses generated – people consider
    only a small subset of possibilities
   Availability heuristic – people make judgments based on how
    easily information is retrieved (e.g., risk of airplane crash)
   Representativeness heuristic – decision based on how
    closely info represents typical outcome
   Overconfidence – individual’s belief that they are correct
    more often than they actually are
 Heuristics
 Hypothesis evaluation/selection
       Cognitive fixation – identifying a hypothesis and
        sticking with it (mind set)
           (application to “Business in Bhopal”)
       Confirmation Bias (cognitive tunnel vision) –
        tendency to seek out only confirming information
           Ex: car won’t start and battery dead, fail to check alternator


                                                                   5                            4
                                                 Dr. Steve Kass               Dr. Steve Kass
                                            University of West Florida   University of West Florida
                                              Pensacola, FL 32514          Pensacola, FL 32514




Hypothesis: If an envelope is sealed, then it has a 5 cent stamp on it.
Turn over the minimum number of envelopes necessary to test this hypothesis
Heuristics
Biases in action selection
    Retrieve a small number of actions – limited by how
     many action plans can be held in working memory
    Availability heuristic for actions – easy to retrieve
     actions are most often chosen
    Availability of possible outcomes – decisions will be
     made based on how memorable the outcome of that
     choice has been in the past
Naturalistic Decision Making
Naturalist Decision Making – research into the way people
  use their experience to make decisions in field settings

Real-world decision making tasks typically include:
   Ill-structured problems
   Uncertain, dynamic environments
   Information-rich environments where situational cues change rapidly
   Cognitive processing that proceeds in iterative action/feedback loops
   Multiple shifting and/or competing individual and organizational goals
   Time constraints or stress
   High risk
   Multiple persons involved in decision
   Rasmussen’s Skill-, Rule-, and
Knowledge-based performance model
 High Novice
                Analytic




                Intuitive




 Low   Expert
                Automatic
Situation Awareness
Situation Awareness – “skilled behavior that encompasses the
processes by which task-relevant information is extracted,
integrated, assessed, and acted upon” (Kass, Herschler, & Companion, 1991).

Levels of SA (Endsley, 1988)
• Level 1 – Awareness of information
• Level 2 – Comprehension of its meaning
• Level 3 – Projection of future status

 SA is Difficult to measure:
 Self-report measures - Only aware of what you are aware of
 Performance-based measures – Intrusive, measure affects performance
Factors Affecting Loss of
Situation Awareness
• Attention
   • attentional demands of controlled processes (k-based performance)
• Pattern Recognition
    • inability to perceive pattern of cues (recognition-primed DM)
• Workload
    • tasks too demanding or too many at once
• Mental models
    • inadequate understanding of system or state
• Working Memory
   • failure to adequately “chunk” information
Examples:
• Commercial plane crashes in the Everglades when aircrew becomes
fixated on a warning light while the plane slowly descends into the ground.
• Outfielder for the Mets tosses ball to a fan after making the second out
while runner on base easily scores.
Improving
Situation Awareness
    Cue Filtering – eliminate irrelevant cues (clutter) that
     interfere with accurate assessment of situation
    Augmented Displays –displays that highlight or overlay
     actual information to make it more salient
    Spatial Organization – arranging displays to capitalize
     on spatial relationships (e.g., pop-out effect)
    Automate Status Updates – as the environment
     changes the system should warn the user of change
    Train Users to Improve Attention?
Methods for Improving
Decision Making
• Redesign – System should make the decision options obvious
    • Problem: No system can rule out all bad options
• Training – Train people to be better decision makers
   • Focus on process measures – not outcome measures
   • Reward correct each DM step, outcome maybe delayed
   • Provide feedback on consequences of bad, as well as good decisions
   • Problem: Decision making is typically task-specific
• Decision Support Systems – Makes available expert
knowledge for DM
   • Problem: People tend to mistrust DSS, or can’t use for novel problems
Decision Aids
                                          Click on dice for decision aid based on
                                          Expected Value Theory


   Decision Tables/Trees – provides calculations of
    possible outcomes that would result from different choices
   Expert Systems – computer programs that use experts’
    knowledge of concepts, principles, and rules
   Decision Support Systems – any interactive system that
    allows you to input problem information which it uses to
    formulate a solution based on complex algorithms

				
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posted:8/3/2011
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