IDEAS home Printed from https://ideas.repec.org/a/taf/jriskr/v15y2012i2p209-222.html
   My bibliography  Save this article

Improving risk matrices: the advantages of logarithmically scaled axes

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13669877.2011.634514
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13669877.2011.634514?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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. 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.
    10. 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).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:jriskr:v:15:y:2012:i:2:p:209-222. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RJRR20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.