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Big data and prediction: four case studies

Northcott, Robert (2019) Big data and prediction: four case studies. [Preprint]

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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.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Northcott, Robertr.northcott@bbk.ac.uk0000-0001-8791-8364
Additional Information: forthcoming in Studies in the History and Philosophy of Science
Keywords: Big data; prediction; case studies; explanation; elections; weather
Subjects: General Issues > Data
General Issues > Causation
Specific Sciences > Complex Systems
Specific Sciences > Climate Science and Meteorology
Specific Sciences > Computer Science
General Issues > Computer Simulation
Specific Sciences > Economics
General Issues > Explanation
Depositing User: Dr Robert Northcott
Date Deposited: 18 Sep 2019 23:53
Last Modified: 18 Sep 2019 23:53
Item ID: 16443
DOI or Unique Handle: https://doi.org/10.1016/j.shpsa.2019.09.002
Subjects: General Issues > Data
General Issues > Causation
Specific Sciences > Complex Systems
Specific Sciences > Climate Science and Meteorology
Specific Sciences > Computer Science
General Issues > Computer Simulation
Specific Sciences > Economics
General Issues > Explanation
Date: 18 September 2019
URI: http://philsci-archive.pitt.edu/id/eprint/16443

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