Modelli trasandati. Perché in scienza, oltre un certo limite, la precisione è deleteria
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It is for sure that any scientific model is very partial, depending upon the choice of a peculiar view point. This implies that the comparison of different models of the same natural phenomenon, allowing for a multiplicity of vantage pints, it is (at least in principle) the most fruitful way to knowledge. Notwithstanding that, to look for a ‘complete description’ obtained by the summation of the different models, is an almost sure guarantee of failure. The neat superiority of predictive models that consciously decide not to take into consideration the major part of the information with respect to computational intensive ‘all-inclusive’ strategies is evident. These ‘all-inclusive’ approaches are promoted by ‘Big Data’ supporters claiming for the need of approaches devoid of any ‘cognitive bias’. Here I will show how the conscious limitation to a specific viewpoint is not only a wise knowledge strategy but mirrors some facts of nature suggesting a self-generated simplification of the natural world that most generative scientific theories are able to catch.
keywordsData Analysis, Incompleteness, Prediction, Errors, UncertaintyBiografia dell'autorealessandro.giuliani@iss.it |
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