of those aspects is integral to the decision-making process and each contributes to an understanding of the role of uncertainty in decision making, the committee looked broadly at those aspects when it considered decision making in the face of uncertainty.


All EPA decisions involve uncertainty, but the types of uncertainty can vary widely among decisions. For an analysis of uncertainty to be useful, a first and critical step is to identify the types of key uncertainties that are involved in a particular decision problem. Understanding the types of the uncertainty that are present will help EPA’s decision makers determine when to invest resources to reduce the uncertainty and how to take that uncertainty into account in their decisions.

In this report, the committee classifies uncertainty in two categories: (1) statistical variability and heterogeneity (also called aleatory or exogenous uncertainty), and (2) model and parameter uncertainty (also called epistemic uncertainty). It also discusses a third category of uncertainty, referred to as deep uncertainty (uncertainty about the fundamental processes or assumptions underlying a risk assessment),32 which is based on the level of uncertainty. Uncertainty stemming either from statistical variability and heterogeneity or from model and parameter uncertainty can be deep uncertainty. Chemical risk assessors typically consider uncertainty and variability to be separate and distinct, but in other fields uncertainty encompasses statistical variability and heterogeneity as well as model and parameter uncertainty (Swart et al., 2009). The committee discusses its rationale for including variability and heterogeneity as one type of uncertainty in Box 1-3.

The three different types of uncertainty are discussed below.

Statistical Variability and Heterogeneity

Variability and heterogeneity, which together are sometimes referred to as aleatory uncertainty, refer to the natural variations in the environment, exposure paths, and susceptibility of subpopulations (Swart et al., 2009). They are inherent characteristics of the system under study, cannot be controlled by decision makers (NRC, 2009; Swart et al., 2009), and cannot be reduced by collecting more information. Empirical estimates of variability and heterogeneity can, however, be better understood through research, thereby refining such estimates.


32 Deep uncertainty has also been called severe uncertainty and hard uncertainty (CCSP, 2009).

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