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Adjusting Triangular Distributions for Judgmental Bias

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  • David G. Hudak

Abstract

The common problem in risk analysis of correctly specifying a probability distribution about an estimate in situations when few data are available is examined. In the absence of data, experts are sometimes used to give a lowest and highest conceivable estimate. Triangular distributions are well suited for these situations when only a low, high, and most likely estimate are given. A problem, however, exists from the failure to adjust for biases when estimating extreme values. Various types of biases, which narrow the range of extreme estimates, are explored. A method is suggested for accounting for these biases by placing extreme estimates at specified percentile points rather than endpoints of a triangular distribution. Since most Monte Carlo models require end points of a triangular distribution, a closed‐form expression for identifying the end points given two percentile points and a most likely point is derived. This method has been used extensively in developing cost risk estimates for the Ballistic Missile Defense Organization (BMDO).

Suggested Citation

  • David G. Hudak, 1994. "Adjusting Triangular Distributions for Judgmental Bias," Risk Analysis, John Wiley & Sons, vol. 14(6), pages 1025-1031, December.
    • Handle: RePEc:wly:riskan:v:14:y:1994:i:6:p:1025-1031
    DOI: 10.1111/j.1539-6924.1994.tb00072.x
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    References listed on IDEAS

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    1. Zarnowitz, Victor, 1979. "An Analysis of Annual and Multiperiod Qtrly Forecasts of Aggregate Income, Output, and the Price Level," The Journal of Business, University of Chicago Press, vol. 52(1), pages 1-33, January.
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