Abstract
Risk matrices used in industry characterize particular risks in terms of the likelihood of occurrence, and the consequence of the 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.
Recommended Citation
E. D. Smith et al., "Risk Matrix Input Data Biases," Systems Engineering, vol. 12, no. 4, pp. 344 - 360, Wiley, Dec 2009.
The definitive version is available at https://doi.org/10.1002/sys.20126
Department(s)
Engineering Management and Systems Engineering
Second Department
Mathematics and Statistics
Publication Status
Full Access
Keywords and Phrases
Cognitive biases; Risk analysis; Risk matrix; Subjective probability; Utility function
International Standard Serial Number (ISSN)
1520-6858; 1098-1241
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Wiley, All rights reserved.
Publication Date
01 Dec 2009
Included in
Mathematics Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Statistics and Probability Commons
