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Risk Management and the Precautionary Principle: A Fuzzy Logic Model

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  • Enrico Cameron
  • Gian Francesco Peloso

Abstract

The aim of this article is to illustrate a procedure for applying the precautionary principle within a strategy for reducing the possibility of underestimating the effective risk caused by a phenomenon, product, or process, and of adopting insufficient risk reduction measures or overlooking their need. We start by simply defining risk as the product between the numerical expression of the adverse consequences of an event and the likelihood of its occurrence or the likelihood that such consequences will occur. Uncertainty in likelihood estimates and several key concepts inherent to the precautionary principle, such as sufficient certainty, prevention, and desired level of protection, are represented as fuzzy sets. The strategy described may be viewed as a simplified example of a precautionary decision process that has been chiefly conceived as a theoretical contribution to the debate concerning the precautionary principle, the quantification of its application, and the formal approach to such problems.

Suggested Citation

  • Enrico Cameron & Gian Francesco Peloso, 2005. "Risk Management and the Precautionary Principle: A Fuzzy Logic Model," Risk Analysis, John Wiley & Sons, vol. 25(4), pages 901-911, August.
  • Handle: RePEc:wly:riskan:v:25:y:2005:i:4:p:901-911
    DOI: 10.1111/j.1539-6924.2005.00607.x
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    Cited by:

    1. Jelena Tašić & Zsófia Nagy-Perjési & Márta Takács, 2024. "Multilevel Fuzzy Inference System for Estimating Risk of Type 2 Diabetes," Mathematics, MDPI, vol. 12(8), pages 1-17, April.
    2. Shital A. Thekdi & James H. Lambert, 2012. "Decision Analysis and Risk Models for Land Development Affecting Infrastructure Systems," Risk Analysis, John Wiley & Sons, vol. 32(7), pages 1253-1269, July.

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