Abstract
One important aspect of risk analysis is the individual and societal comparison of probability distributions over a set of undesirable outcomes. To compare all possible distributions it is mandatory to assign one single value (called an index) to each distribution. Typical indices are the expected value and the expected utility model; the paper notes some theoretical and practical limitations of these indices. The paper briefly describes the author’s linearized moments model (LMM) that gives consideration to individual risk perceptions and that takes into account the shape of the probability distribution including the low-probability high-consequence tail. The LMM solves the main theoretical and practical inconsistencies of the expected utility model. It is noted that current indices like the expected value, the expected utility model and the prospect theory are special cases of the LMM.
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© 1991 Springer Science+Business Media New York
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Munera, H.A. (1991). A General Model for Quantitative Risk Comparisons. In: Zervos, C., Knox, K., Abramson, L., Coppock, R. (eds) Risk Analysis. Advances in Risk Analysis, vol 8. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-0730-1_46
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DOI: https://doi.org/10.1007/978-1-4899-0730-1_46
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