Cognitive Biases in Decision Making William Siefert, M. S

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Cognitive Biases in Decision Making

  • William Siefert, M.S.


  • 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

5 x 5 Risk “Cube”

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


  • More accurate and repeatable Systems Engineering Decisions

    • Confidence in correct assessment of probability and value
    • Avoidance of specific mistakes
    • Recommended actions

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.“


  • 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.

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.

  • It is important to note that subjects often maintain a strong sense that they are acting rationally while exhibiting biases.


  • Frequency of occurrence is objective, discrete

  • Probability is continuous, fiction

    • "Humans judge probabilities poorly" [Cosmides and Tooby, 1996]
  • Likelihood is a subjective judgment

    • (unless mathematical)
    • 'Exposure' by project manager
      • timeless

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

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

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

Severity Amplifiers

  • Lack of control

  • Lack of choice

  • Lack of trust

  • Lack of warning

  • Lack of understanding

  • Manmade

  • Newness

  • Dreadfulness

  • Personalization

  • Recallability

  • Imminency

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

Prospect Theory

  • Decision-making described with subjective assessment of:

    • Probabilities
    • Values
      • and combinations in gambles
  • Prospect Theory breaks subjective decision making into:

    • preliminary ‘screening’ stage,
      • probabilities and values are subjectively assessed
    • secondary ‘evaluation’ stage
      • combines the subjective probabilities and utilities

Humans judge probabilities poorly*

Gains and losses are not equal*

Subjective Utility

  • Values considered from reference point established by the subject’s wealth and perspective

    • Framing
  • Gains and losses are

  • subjectively valued

    • 1-to-2 ratio.

Implication of Prospect Theory for the Risk Matrix


  • Impediments for the appearance of cognitive biases in the industry data:

    • Industry data are granular while the predictions of Prospect Theory are for continuous data
    • Qualitative descriptions of 5 ranges of likelihood and consequence
  • Nevertheless, the evidence of cognitive biases emerges from the data

3. Probability Centering Bias

  • Likelihoods are pushed toward

    • L = 3
  • Symmetric to a first order

Cognitive Biases in Action

  • Engineers:

    • Schedule consequences effect careers
    • Technical consequences effect job performance reviews
    • Cost consequences are remote and associated with management
  • Higher cognizance of Biases will be valuable at the engineering level


  • 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.

Suggestions for Cognitive Biases improvement

  • Long-term, institutional rationality

  • Team approach

  • Iterations

  • Public review

  • Expert review

  • Biases and errors awareness

    • Requires cultural changes


  • 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:

  • 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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