Evaluation (not validation) of quantitative models.
The present regulatory climate has led to increasing demands for scientists to attest to the predictive reliability of numerical simulation models used to help set public policy, a process frequently referred to as model validation. But while model validation may reveal useful information, this paper argues that it is not possible to demonstrate the predictive reliability of any model of a complex natural system in advance of its actual use. All models embed uncertainties, and these uncertainties can and frequently do undermine predictive reliability. In the case of lead in the environment, we may categorize model uncertainties as theoretical, empirical, parametrical, and temporal. Theoretical uncertainties are aspects of the system that are not fully understood, such as the biokinetic pathways of lead metabolism. Empirical uncertainties are aspects of the system that are difficult (or impossible) to measure, such as actual lead ingestion by an individual child. Parametrical uncertainties arise when complexities in the system are simplified to provide manageable model input, such as representing longitudinal lead exposure by cross-sectional measurements. Temporal uncertainties arise from the assumption that systems are stable in time. A model may also be conceptually flawed. The Ptolemaic system of astronomy is a historical example of a model that was empirically adequate but based on a wrong conceptualization. Yet had it been computerized--and had the word then existed--its users would have had every right to call it validated. Thus, rather than talking about strategies for validation, we should be talking about means of evaluation. That is not to say that language alone will solve our problems or that the problems of model evaluation are primarily linguistic. The uncertainties inherent in large, complex models will not go away simply because we change the way we talk about them. But this is precisely the point: calling a model validated does not make it valid. Modelers and policymakers must continue to work toward finding effective ways to evaluate and judge the quality of their models, and to develop appropriate terminology to communicate these judgments to the public whose health and safety may be at stake.
Oreskes
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Gallatin School of Individualized Study, New York University, New York, USA. noreskes@ucsd.edu
D008954:Models, Biological; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D014116:Toxicology