risk biases siefert
TRANSCRIPT
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CognitiveCognitive
BiasesBiasesin Decisionin DecisionMakingMaking
William Siefert, M.S.
Consequence
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Acknowledgements
Work based on the research done by
Dr Amos Tversky, PhD
Dr Daniel Kahneman, PhD
³Prospect Theory´ Nobel Prize, 2002
Dr Eric Smith, PhD
Dr Paul Slovic, PhD
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³Fear of harm ought to be
proportional not merely to thegravity 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 ³Cube´
Consequence
5
Original
Current
Objectivevs.
Subjective
data
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Present Situation
Risk matrices are recognized by industryas the best way to:
consistently quantify risks, as part of a
repeatable and quantifiable risk management
processRisk matrices involve human:
Numerical judgment
Calibration ± location, gradation
Rounding, Censoring
Data updatingoften approached with under confidence
often distrusted by decision makers
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Goal
M
ore accurate and repeatable SystemsEngineering 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 inEconomics in 2002 "for having integratedinsights from psychological research intoeconomic science, especially concerning
human judgment and decision-makingunder uncertainty.³
Similarities between
cognitive bias experimentsand 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 expertopinion 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 compromiseestimates, judgments and decisions.
It is important to note that subjects oftenmaintain a strong sense that they are
acting rationally while exhibiting biases.
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Likelihood
1.F
requency 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 pro ject manager timeless
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Case Study
Industry risk matrix data1412 original and current risk points
Time of first entry known
Time of last update known
Cost, Schedule and Technical knownSubject matter not known
Biases revealedLikelihood and consequence judgment
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Magnitude vs. Reliability [Griffin and Tversky, 1992]
M
agnitude 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 youwatch 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 5R
iskM
atrices seek to increaserisk 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
D
ecision-making described withsubjective assessment of:
Probabilities
Values
and combinations in gambles
Prospect Theory breaks subjective
decision making into:
1) preliminary µscreening¶ stage, probabilities and values are subjectively assessed
2) secondary µevaluation¶ stage
combines the subjective probabilities and utilities
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Humans judge probabilities poorly*
Small probabilities
overestimated
Large probabilities
under estimated
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Gains and losses are not equal*
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Subjective Utility
Values considered from
reference pointestablished by the
subject¶s wealth and
perspectiveFraming
Gains and losses are
subjectively valued1-to-2 ratio.
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Implication of Pros pect Theory for the Risk Matrix
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ANALYSES AND OBSERVATIO NS
OF INITIAL DATA
Impediments for the appearance of cognitivebiases in the industry data:
1) Industry data are granular while the predictions
of Prospect Theory are for continuous data2) 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
Likelihoodsare pushed
towardL = 3
Symmetric
to a firstorder
L kelihood Mar i al Distri utio
of Ori i al Poi ts
0
1
2
3
4
5
6
-1 0 1 2 3 4 5 6
o se ue ce
L i k e l i h o o
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Open Ri nth
0
10
20
30
40
50
60
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77
Series2
Linear Series2)
Guess Why the Spike in New Risks
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Cognitive Biases in Action
Engineers:
1. Schedule consequenceseffect careers
2. Technicalconsequences effect jobperformance reviews
3. Cost consequences areremote and associatedwith management
Higher cognizance of Biases will be valuable atthe engineering level
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CO NCLUSIO N
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 riskitems 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 fromthe literature on judgment under uncertainty, Cognition 58(1996), 1-73.
D. Kahneman, and A. Tversky, Prospect theory: Ananalysis of decision under risk, Econometrica 46(2) (1979),171-185.
Nobel, "The Bank of Sweden Prize in Economic Sciencesin 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 toquestionaire construction, Review of Personality andSocial Psychology 11 (1990), 98-119.
A. Tversky, and D. Kahneman, Advances in prospecttheory: Cumulative representation of uncertainty, Journal
of R
isk and Uncertainty 5 (1992), 297-323
.