Decision Making
Human Factors Psychology
Dr. Steve
Historically Bad Decisions
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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
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Decision making – selecting one choice from a
number of choices involving some level of uncertainty.
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Intuitive Decision Making – quick and relatively
automatic responses to a problem.
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Ex: Response to yellow traffic light
Analytic Decision Making – slow, deliberate, and
controlled responses to a problem.
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Ex: What stock to purchase
Rational Decision Making
Research
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Normative Decision Models – Assumes individuals
act rationally in trying to find the best solution to
optimize outcome.
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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
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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
(0 to 1)
(-10 to +10)
PxU
Use Safety Device
Alternative
Expected
Utility
-1.7
Accident: Injury or death
is prevented
.10
+10.0
+1.0
No Accident: inefficient,
restrictive, discomfort
.90
-3.0
-2.7
Not Use Safety Device
+5.3
Accident: Serious injury or
death
.10
-10.0
-1.0
No Accident: more
efficient and comfortable
.90
+7.0
+6.3
Model predicts workers will not use the safety device
What if probability of an accident was .50?
Descriptive Decision Models
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Descriptive Decision Models – Assume humans do not
act rationally in decision making (assumptions of normative
models are violated)
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Framing Effects – The way a problem is phrased affects the
decision
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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
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Ex: Decision as to how well I should define a term
Algorithms vs. Heuristics
Solve the anagram: metssy
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Algorithms are procedures that will always lead to
correct answer
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Ex: Try every combination – metssy mtssye mssyet msyets
….
Heuristics are shortcuts that are not guaranteed to
lead to best answer, but are more efficient
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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
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Amount of cue info brought into Working Memory
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Time available for decision-making
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Function of how many tasks are occurring concurrently
LTM retrieval ability
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Function of the time criticality of task
Attentional resources
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Function of attentional demands
Person may possess right info, but fail to retrieve it (inert knowledge)
Working Memory Capacity
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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
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Outcome produces maximum value
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Problem is that decision are often made to avoid worst
outcome rather than maximize value
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Positive vs. Negative outcome
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Problem is decision may be positive in the short term, but
turn out to be a big mistake later
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Ex: Decision to buy the extended warranty on an appliance
Ex: Japan’s decision to attack U.S. in 1941
Comparison to expert’s decisions
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Problem is that experts don’t always make good decisions
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Ex: Experts’ decision to launch Challenger
Heuristics
Biases in using cues for DM
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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
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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
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Cognitive fixation – identifying a hypothesis and
sticking with it (mind set)
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(application to “Business in Bhopal”)
Confirmation Bias (cognitive tunnel vision) –
tendency to seek out only confirming information
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Ex: car won’t start and battery dead, fail to check alternator
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Dr. Steve Kass
University of West Florida
Pensacola, FL 32514
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Dr. Steve Kass
University of West Florida
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
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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:
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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
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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
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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