Predicting the future state of a system has always been a natural motivation for science and practical applications. Such a topic, beyond its obvious technical and societal relevance, is also interesting from a conceptual point of view. This owes to the fact that forecasting lends itself to two equally radical, yet opposite methodologies. A reductionist one, based on first principles, and the naïve-inductivist one, based only on data. This latter view has recently gained some attention in response to the availability of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. The purpose of this note is to assess critically the role of big data in reshaping the key aspects of forecasting and in particular the claim that bigger data leads to better predictions. Drawing on the representative example of weather forecasts we argue that this is not generally the case. We conclude by suggesting that a clever and context-dependent compromise between modelling and quantitative analysis stands out as the best forecasting strategy, as anticipated nearly a century ago by Richardson and von Neumann.
Forecasting in light of Big Data / H. Hosni, A. Vulpiani. - In: PHILOSOPHY & TECHNOLOGY. - ISSN 2210-5433. - 31:4(2018 Dec 01), pp. 557-569.
|Titolo:||Forecasting in light of Big Data|
|Parole Chiave:||forecasting; Big data; epistemology|
|Settore Scientifico Disciplinare:||Settore M-FIL/02 - Logica e Filosofia della Scienza|
|Data di pubblicazione:||1-dic-2018|
|Data ahead of print / Data di stampa:||26-mag-2017|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/s13347-017-0265-3|
|Appare nelle tipologie:||01 - Articolo su periodico|
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