Authors
Robert Northcott
Birkbeck, University of London
Abstract
Has the rise of data-intensive science, or ‘big data’, revolutionized our ability to predict? Does it imply a new priority for prediction over causal understanding, and a diminished role for theory and human experts? I examine four important cases where prediction is desirable: political elections, the weather, GDP, and the results of interventions suggested by economic experiments. These cases suggest caution. Although big data methods are indeed very useful sometimes, in this paper’s cases they improve predictions either limitedly or not at all, and their prospects of doing so in the future are limited too.
Keywords Big data  prediction  case studies  explanation  elections  weather
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DOI 10.1016/j.shpsa.2019.09.002
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References found in this work BETA

Making Models Count.Anna Alexandrova - 2008 - Philosophy of Science 75 (3):383-404.
The Causal Nature of Modeling with Big Data.Wolfgang Pietsch - 2016 - Philosophy and Technology 29 (2):137-171.
Aspects of Theory-Ladenness in Data-Intensive Science.Wolfgang Pietsch - 2015 - Philosophy of Science 82 (5):905-916.

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Citations of this work BETA

The Fate of Explanatory Reasoning in the Age of Big Data.Frank Cabrera - 2021 - Philosophy and Technology 34 (4):645-665.

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