Adjusting Triangular Distributions for Judgmental Bias

David G. Hudak

Risk Analysis, 1994, vol. 14, issue 6, 1025-1031

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).

Date: 1994
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https://doi.org/10.1111/j.1539-6924.1994.tb00072.x

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Persistent link: https://EconPapers.repec.org/RePEc:wly:riskan:v:14:y:1994:i:6:p:1025-1031

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