An introduction to second‐order random variables in human health risk assessments

Abstract

When performing a human health risk assessment using probabilistic methods, risk assessors need a way to distinguish, analyze, and visualize both the variability and the uncertainty in a quantity. As described by many previous authors, first‐order random variables represent variability, i.e., the heterogeneity or diversity in a well‐characterized population which is usually not reducible through further measurement or study. Growing in popularity, second‐order random variables also include uncertainty, i.e., partial ignorance or lack of perfect knowledge about a poorly characterized phenomenon which may be reducible through further study. In this paper, we explore second‐order random variables as a way to encode and propagate variability and uncertainty separately.

Key words:

• variability
• uncertainty
• second‐order random variable
• Monte Carlo simulation

Notes

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