Risk matrix input data biases

Eric D. Smith, William T. Siefert and David Drain

Systems Engineering, 2009, vol. 12, issue 4, 344-360

Abstract: Risk matrices used in industry characterize particular risks in terms of the likelihood of occurreRisk matrix input data biaseshe actualized risk. Human cognitive bias research led by Daniel Kahneman and Amos Tversky exposed systematic translations of objective probability and value as judged by human subjects. Applying these translations to the risk matrix allows the formation of statistical hypotheses of risk point placement biases. Industry‐generated risk matrix data reveals evidence of biases in the judgment of likelihood and consequence—principally, likelihood centering, a systematic increase in consequence, and a diagonal bias. Statistical analyses are conducted with linear regression, normal distribution fitting, and Bayesian analysis. Evidence presented could improve risk matrix based risk analysis prevalent in industry. © 2008 Wiley Periodicals, Inc. Syst Eng

Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://doi.org/10.1002/sys.20126

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:syseng:v:12:y:2009:i:4:p:344-360

Access Statistics for this article

More articles in Systems Engineering from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().