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High Risk
4
Likelihood
Cognitive
Biases
in Decision
Making
5
Moderate Risk
3
2
Low Risk
1
1
William Siefert, M.S.
2
3
4
Consequence
5
Acknowledgements
• Work based on the research done by
 Dr Amos Tversky, PhD
 Dr Daniel Kahneman, PhD
“Prospect Theory” Nobel Prize, 2002
 Dr Eric Smith, PhD
 Dr Paul Slovic, PhD
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“Fear of harm ought to be
proportional not merely to the
gravity of the harm, but also to the
probability of the event.”
Logic, or the Art of Thinking
Antoine Arnould, 1662
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5 x 5 Risk “Cube”
Original
5
High Risk
Likelihood
4
Current
Moderate Risk
3
2
Objective
vs.
Subjective
data
Low Risk
1
1
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3
4
Consequence
5
5
Present Situation
• Risk matrices are recognized by industry
as the best way to:
consistently quantify risks, as part of a
repeatable and quantifiable risk management
process
• Risk matrices involve human:
Numerical judgment
Calibration – location, gradation
Rounding, Censoring
Data updating
often approached with under confidence
often distrusted by decision makers
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Goal
• More accurate and repeatable Systems
Engineering Decisions
Confidence in correct assessment of
probability and value
Avoidance of specific mistakes
Recommended actions
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Heuristics and Biases
Daniel Kahneman won the Nobel Prize in
Economics in 2002 "for having integrated
insights from psychological research into
economic science, especially concerning
human judgment and decision-making
under uncertainty.“
Similarities between
cognitive bias experiments
and the risk matrix axes
show that risk matrices are
susceptible to human
biases.
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Anchoring
• First impression dominates all further thought
• 1-100 wheel of fortune spun
• Number of African nations in the United Nations?
 Small number, like 12, the subjects underestimated
 Large number, like 92, the subjects overestimated
• Obviating expert opinion
• The analyst holds a circular belief that expert
opinion or review is not necessary because no
evidence for the need of expert opinion is present.
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Heuristics and Biases
Presence of cognitive biases
– even in extensive and vetted analyses –
can never be ruled out.
Innate human biases, and exterior
circumstances, such as the framing or
context of a question, can compromise
estimates, judgments and decisions.
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It is important to note that subjects often
maintain a strong sense that they are
acting rationally while exhibiting biases.
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Likelihood
1. Frequency of occurrence is objective,
discrete
2. Probability is continuous, fiction
 "Humans judge probabilities poorly"
[Cosmides and Tooby, 1996]
3. Likelihood is a subjective judgment
(unless mathematical)
 'Exposure' by project manager
 timeless
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Case Study
• Industry risk matrix data
1412 original and current risk points
Time of first entry known
Time of last update known
Cost, Schedule and Technical known
Subject matter not known
• Biases revealed
Likelihood and consequence judgment
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Magnitude vs. Reliability [Griffin and Tversky, 1992]
• Magnitude perceived more valid
• Data with outstanding magnitudes but
poor reliability are likely to be chosen
and used
• Observation: risk matrices are
magnitude driven, without regard to
reliability
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1. Estimation in a Pre-Define Scale Bias
• Scale magnitude effects judgment [Schwarz, 1990]
• Two questions, random 50% of subjects:
• Please estimate the average number of hours you
watch television per week:
____
____
__X_
____
____
____
1-4
5-8
9-12
13-16
17-20
More
• Please estimate the average number of hours you
watch television per week:
____
____
__X_
____
____
____
1-2
3-4
5-6
7-8
9-10
More
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Severity Amplifiers
• Lack of control
• Lack of choice
• Lack of trust
• Lack of warning
• Lack of understanding
• Manmade
• Newness
• Dreadfulness
• Personalization
• Recallability
• Imminency
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Situation assessment
• 5 x 5 Risk Matrices seek to increase
risk estimation consistency
• Hypothesis: Cognitive Bias
information can help improve the
validity and sensitivity of risk matrix
analysis and other Systems
Engineering analysis
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Prospect Theory
• Decision-making described with
subjective assessment of:
 Probabilities
 Values
 and combinations in gambles
• Prospect Theory breaks subjective
decision making into:
1) preliminary ‘screening’ stage,
 probabilities and values are subjectively assessed
2) secondary ‘evaluation’ stage
 combines the subjective probabilities and utilities
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Subjective Probability Weighting
Humans judge probabilities poorly*
1.0
Small probabilities
overestimated
Typical
Estimate
Ideal Estimate
0.0
0.0
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Large probabilities
under estimated
1.0
Real Probability
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Gains and losses are not equal*
Subjective
Worth
Gains
Objective
Value
Reference
Point
Losses
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Subjective Utility
• Values considered from
reference point
established by the
Subjective
subject’s wealth and
Utility, U(v)
perspective
Framing
• Gains and losses are
subjectively valued
1-to-2 ratio.
Losses
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Gains
Objective
Value, v
Reference Point
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Implication of Prospect Theory for the Risk Matrix
Risk Cube
1.0
Probability
Estimated
probability
General
Tendencies for
Engineers
0.0
Actual probability
Severity
1.0
Utility
0.0
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Value
Engineer,
Non-Ownership
Viewpoint
Losses
CEO,
Company Ownership
Viewpoint
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ANALYSES AND OBSERVATIONS
OF INITIAL DATA
Impediments for the appearance of cognitive
biases in the industry data:
1) Industry data are granular while the predictions
of Prospect Theory are for continuous data
2) Qualitative descriptions of 5 ranges of
likelihood and consequence
 non-linear influence in the placement of risk datum
points
Nevertheless, the evidence of cognitive
biases emerges from the data
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3. Probability Centering Bias
LIkelihood Marginal Distribution
of Original Points
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• Symmetric
to a first
order
5
4
Likelihood
• Likelihoods
are pushed
toward
L=3
3
2
1
0
-1
0
1
2
3
Consequence
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5
6
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Guess Why the Spike in New Risks
Open Risks by Month
60
50
40
Series2
30
Linear (Series2)
20
10
0
1
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9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77
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Cognitive Biases in Action
•
Engineers:
1. Schedule consequences
effect careers
2. Technical
consequences effect job
performance reviews
3. Cost consequences are
remote and associated
with management
 Higher cognizance of
Biases will be valuable at
the engineering level
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CONCLUSION
• First time that the effects of cognitive biases
have been documented within the risk matrix
• Clear evidence that probability and value
translations, as likelihood and consequence
judgments, are present in industry risk matrix
data
• Steps
 1) the translations were predicted by prospect theory, 2)
historical data confirmed predictions
• Risk matrices are not objective number grids
• Subjective, albeit useful, means to verify that risk
items have received risk-mitigating attention.
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Suggestions for Cognitive Biases improvement
• Long-term, institutional rationality
• Team approach
• Iterations
• Public review
• Expert review
• Biases and errors awareness
Requires cultural changes
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References
• L. Cosmides, and J. Tooby, Are humans good intuitive
statisticians after all? Rethinking some conclusions from
the literature on judgment under uncertainty, Cognition 58
(1996), 1-73.
• D. Kahneman, and A. Tversky, Prospect theory: An
analysis of decision under risk, Econometrica 46(2) (1979),
171-185.
• Nobel, "The Bank of Sweden Prize in Economic Sciences
in memory of Alfred Nobel 2002," 2002. Retrieved March,
2006 from Nobel Foundation:
http://nobelprize.org/economics/laureates/2002/index.html.
• N. Schwarz, Assessing frequency reports of mundane
behaviors: Contributions of cognitive psychology to
questionaire construction, Review of Personality and
Social Psychology 11 (1990), 98-119.
• A. Tversky, and D. Kahneman, Advances in prospect
theory: Cumulative representation of uncertainty, Journal
of Risk and Uncertainty 5 (1992), 297-323.
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• Comments !
• Questions ?
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