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Verification, validation, and confirmation of numerical models in the Earth sciences
Science (New York, N.Y.)
|February 4, 1994
Summary
Numerical models of natural systems cannot be fully verified or validated due to their inherent complexity and non-unique results. While models can be confirmed by matching predictions to observations, this confirmation is always partial, highlighting their heuristic value.
Area of Science:
- Environmental modeling
- Computational science
- Philosophy of science
Background:
- Numerical models are widely used to simulate natural systems.
- The rigorous verification and validation of these models are crucial for their reliable application.
- Challenges exist in establishing the absolute accuracy of models representing complex, open systems.
Purpose of the Study:
- To critically evaluate the concepts of verification and validation as applied to numerical models of natural systems.
- To explore the limitations and implications of model confirmation in scientific practice.
- To redefine the primary utility of scientific models.
Main Methods:
- Logical analysis of the concepts of verification, validation, and confirmation.
- Examination of the philosophical underpinnings of scientific modeling, including the problem of induction and affirming the consequent.
- Discussion of the inherent characteristics of natural systems (e.g., openness) and model outputs (e.g., non-uniqueness).
Main Results:
- Complete verification and validation of natural system models are logically impossible.
- Natural systems are open, and model results are often non-unique, precluding absolute certainty.
- Model confirmation, based on agreement between prediction and observation, is partial and subject to logical fallacies.
- The predictive value of models remains inherently uncertain.
Conclusions:
- Models of natural systems can only be evaluated in relative terms, not absolutely validated.
- The primary value of numerical models lies in their heuristic function—aiding understanding and inquiry—rather than definitive truth.
- Scientific practice must acknowledge the inherent limitations in model certainty and focus on their role in advancing knowledge.
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