# Decision Making by cuiliqing

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```									Decision Making
Human Factors Psychology
Dr. Steve

•   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
   Ex: Try every combination – metssy mtssye mssyet msyets
….
   Heuristics are shortcuts that are not guaranteed to
   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
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)
• 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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