According to Naomi Oreskes [2000] ‘[… ] models are complex amalgam of theoretical and phenomenological laws (and the governing equations and algorithms that represent them), empirical input parameters, and a model conceptualization. When a model generates a prediction, of what precisely is the prediction a test? The laws? The input data? The conceptualization? Any part (or several parts) of the model might be in error, and there is no simple way to determine which one it is’. This paper advocate the need to use sensitivity analysis to gauge the robsutness of models.
SALTELLI Andrea;
2013-08-12
Editions Quae
JRC74773
978-2-7592-1906-3,
1952-1251,
https://publications.jrc.ec.europa.eu/repository/handle/JRC74773,
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