Abstract
Anyone applying for life insurance knows he or she faces an interview. The insurance agent will inquire about age, family history of diseases, health problems and medical history, smoking behaviour, occupation, lifestyle and many other details. These data are then fed into sophisticated mathematical models to calculate premiums and royalties based on the personal health risks of the individual. With the financial stakes high for both the insurer and the insured, this creates a level playing field based on realistic expectations that is essential for the mutual benefit of both parties. Similar to life insurers, many financial, pharmaceutical and other businesses use such risk‐analysis‐based realistic data to calculate the possibilities of risk or harm in contrast to benefits and/or costs. > The main challenge facing environmental risk assessment is the extrapolation of data In striking contrast to risk modelling based on gathering as much useful real‐world data as possible, the field of environmental risk assessment, which governs the quality of community air and water, the safety of food and the clean‐up of contaminated sites, has been based principally on unverifiable assumptions and speculations. The main challenge facing environmental risk assessment is the extrapolation of data. Regulators must extrapolate results not only from animal toxicity studies, typically from mice and/or rats to humans, but also from the very high doses usually used in animal experiments to the very low doses that are characteristic of human exposure. These two types of extrapolation are steeped in uncertainty. The failure of regulatory agencies during the past three decades, when risk assessment was first applied to environmental regulations, to resolve in reasonable measure these uncertainties has led to a protectionist public health philosophy in which conservative assumptions became accepted at each point in the risk assessment process. The cascade of risks resulting from such a protectionist …

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