Consequences of the Stochastic Nature of Risk. F. A. Seiler, Sigma Five Consulting; and J. L. Alvarez, Auxier & Associates
Every measured quantity is stochastic and can be characterized by three numbers: Some central value, a random and a systematic error. These three numbers lead to important logical consequences for any stochastic quantity such as a risk. First, it is impossible to demonstrate a zero value for any stochastic quantity, and the often voiced stakeholder request for "zero risk" cannot be met. All that can be shown with an appropriate statistical test is that the null hypothesis of "zero risk" cannot be rejected at the level of confidence chosen. There is thus a finite risk value that could not possibly be seen in this test at a meaningful confidence level. Second, from the same data, it is possible to define a minimum statistically significant risk. Its value also depends on the uncertainties of the data and on an appropriately different level of confidence. The important consequence of this definition is that any smaller physical risk is scientifically meaningless. Third, the main task of risk management is to control risks and lower them to a predetermined level. Controlling risk requires being able to raise or lower some particular risk factor. Simple control theory then leads to the concept of a minimum controllable risk which is always somewhat larger than the minimum significant risk. The important consequence of this minimum controllable risk is that any smaller risk cannot possibly be controlled. As an example, we use the risk of radiation carcinogenesis, probably the best known human cancer risk. The minimum significant risk and the minimum controllable risk of radiation cancer are estimated on the basis of experimental data and compared to regulatory limits. They are found to lie above a risk of a few percent, and thus lie several orders of magnitude above regulatory risk levels. Due to this logical mismatch, we are facing some rather serious ethical problems.
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