1. Human Factors Psychology Dr. Steve
Decision Making
Human Factors Psychology
Dr. Steve

2. 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

3. 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

4. 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

5. Subjective Expected Utility Theory
Alternative/Outcomes
Probability
(0 to 1)
Utility
(-10 to +10)
P x U
Alternative Expected Utility
Use Safety Device
-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
-10.0
-1.0
No Accident: more efficient and comfortable
+7.0
+6.3
Model predicts workers will not use the safety device
What if probability of an accident was .50?

6. 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

7. 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

8. 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

9. 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?

10. 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

11. 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

12. 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

13. 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
Dr. Steve Kass
University of West Florida
Pensacola, FL 32514 4 5
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

14. 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

15. 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

16. Rasmussen’s Skill-, Rule-, and Knowledge-based performance model
High
Novice
Analytic
Performance
Attentional Demand
Intuitive
Low
Expert
Automatic

17. 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

18. 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.

19. 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?

20. 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

21. 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