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Evaluation (not validation) of quantitative models

¹Gallatin School of Individualized Study, New York University, New York, USA. noreskes@ucsd.edu

Environmental Health Perspectives

|December 23, 1998

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Predictive reliability of complex natural system models cannot be proven before use due to inherent uncertainties. Focusing on model evaluation, not just validation, is crucial for public policy decisions.

Area of Science:

Environmental Science
Computational Science
Risk Assessment

Background:

Increasing regulatory demands require scientists to validate numerical simulation models for public policy.
Model validation is a process to attest to the predictive reliability of these models.
Complex natural systems pose unique challenges for model validation.

Purpose of the Study:

To argue that demonstrating predictive reliability of complex natural system models in advance is not possible.
To highlight the inherent uncertainties in all models.
To propose a shift from model validation to model evaluation.

Main Methods:

Categorization of model uncertainties: theoretical, empirical, parametrical, and temporal.
Analysis of historical examples, such as the Ptolemaic system, to illustrate conceptual flaws.

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Discussion of the limitations of current validation terminology.

Main Results:

All models of complex natural systems contain uncertainties that can undermine predictive reliability.
Theoretical, empirical, parametrical, and temporal uncertainties are identified as key challenges.
A model can be empirically adequate but conceptually flawed, questioning the concept of validation.

Conclusions:

It is impossible to demonstrate the predictive reliability of complex natural system models before their actual use.
The focus should shift from 'model validation' to 'model evaluation' to better assess model quality.
Clearer terminology is needed to communicate model limitations and uncertainties to policymakers and the public.