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Improving risk matrices: the advantages of logarithmically scaled axes

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  • E.S. Levine

Abstract

Risk matrices are a common tool used throughout the public and private sector to assess and manage risk qualitatively. However, these matrices have well-documented shortcomings when used for either assessment or management that can be shown by assuming a quantitative scale for the likelihood and consequence axes. This article describes the construction of a logarithmically scaled risk assessment matrix which alleviates some of the limitations inherent in using linearly structured risk matrices. In particular, logarithmic risk matrices can better differentiate between hazards with a large dynamic range in risks and, when used in combination with a new categorization scheme, the categorization of risks is more straightforward. These properties are demonstrated using a hypothetical example. Finally, the defensibility of logarithmic matrices is examined in the context of previously proposed rules for developing risk matrices.

Suggested Citation

  • E.S. Levine, 2012. "Improving risk matrices: the advantages of logarithmically scaled axes," Journal of Risk Research, Taylor & Francis Journals, vol. 15(2), pages 209-222, February.
    • Handle: RePEc:taf:jriskr:v:15:y:2012:i:2:p:209-222
    DOI: 10.1080/13669877.2011.634514
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    Cited by:

    1. Mauricio Moraes Davidovich & William K. Klimack, 2022. "PRISM: improved risk management," SN Business & Economics, Springer, vol. 2(7), pages 1-25, July.
    2. E. S. Levine & Julie F. Waters, 2013. "Managing Risk at the Tucson Sector of the U.S. Border Patrol," Risk Analysis, John Wiley & Sons, vol. 33(7), pages 1281-1292, July.
    3. F. Acebes & J. M. González-Varona & A. López-Paredes & J. Pajares, 2024. "Beyond probability-impact matrices in project risk management: A quantitative methodology for risk prioritisation," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    4. Ehre, Max & Papaioannou, Iason & Straub, Daniel, 2020. "A framework for global reliability sensitivity analysis in the presence of multi-uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    5. Strelnik, Mikhail, 2014. "Approving the ISDWIR Method of Risk Measurement in Making Risk Management Decision || Aprobación del método de medición del riesgo SIIPDR en el manejo de asunción de riesgos," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 17(1), pages 42-59, June.
    6. Jianping Li & Chunbing Bao & Dengsheng Wu, 2018. "How to Design Rating Schemes of Risk Matrices: A Sequential Updating Approach," Risk Analysis, John Wiley & Sons, vol. 38(1), pages 99-117, January.
    7. Shabnam Vatanpour & Steve E. Hrudey & Irina Dinu, 2015. "Can Public Health Risk Assessment Using Risk Matrices Be Misleading?," IJERPH, MDPI, vol. 12(8), pages 1-14, August.
    8. Maria-Teresa Bosch-Badia & Joan Montllor-Serrats & Maria-Antonia Tarrazon-Rodon, 2020. "Risk Analysis through the Half-Normal Distribution," Mathematics, MDPI, vol. 8(11), pages 1-27, November.
    9. Žužek Tena & Rihar Lidija & Berlec Tomaž & Kušar Janez, 2020. "Standard Project Risk Analysis Approach," Business Systems Research, Sciendo, vol. 11(2), pages 149-158, October.
    10. David J. Ball & John Watt, 2013. "Further Thoughts on the Utility of Risk Matrices," Risk Analysis, John Wiley & Sons, vol. 33(11), pages 2068-2078, November.
    11. Alan J. Card & James R. Ward & P. John Clarkson, 2014. "Trust‐Level Risk Evaluation and Risk Control Guidance in the NHS East of England," Risk Analysis, John Wiley & Sons, vol. 34(8), pages 1469-1481, August.

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