Benefit-cost analysis and decision-making under risk uncertainty: Issues and illustrations

Abstract

In determining how to set regulatory standards or guidelines for contaminants found in drinking water supplies, decision-makers typically try to balance public health protection benefits against costs. This is a difficult task given the types and degrees of uncertainty (as well as variability) that underlie the assessment of the health risks posed by contaminants. This paper describes methods that use probabilistic approaches (such as Monte Carlo techniques) to describe the types and levels of uncertainties that exist at each stage of a public health-oriented benefit-cost analysis (BCA). This research focuses on how uncertainty and variability need to be characterized, and how propagation of uncertainties often leads to broad confidence intervals for benefit estimates. This information is then assessed within the decision-making context of public health officials and regulators, to reveal how complex probabilistic analysis of benefits and costs can be presented and interpreted in the face of extensive uncertainties.