Format:
1 online resource (77 pages)
,
digital, PDF file(s).
ISBN:
9781108588676
,
9781108706698
Series Statement:
Cambridge elements. Elements in the philosophy of science
Content:
Big Data and methods for analyzing large data sets such as machine learning have in recent times deeply transformed scientific practice in many fields. However, an epistemological study of these novel tools is still largely lacking. After a conceptual analysis of the notion of data and a brief introduction into the methodological dichotomy between inductivism and hypothetico-deductivism, several controversial theses regarding big data approaches are discussed. These include, whether correlation replaces causation, whether the end of theory is in sight and whether big data approaches constitute entirely novel scientific methodology. In this Element, I defend an inductivist view of big data research and argue that the type of induction employed by the most successful big data algorithms is variational induction in the tradition of Mill's methods. Based on this insight, the before-mentioned epistemological issues can be systematically addressed.
Note:
Title from publisher's bibliographic system (viewed on 05 Feb 2021)
Additional Edition:
ISBN 9781108706698
Additional Edition:
Erscheint auch als Druck-Ausgabe Pietsch, Wolfgang Big data Cambridge : Cambridge University Press, 2021 ISBN 9781108706698
Language:
English
Subjects:
Sociology
Keywords:
Big Data
;
Induktion
DOI:
10.1017/9781108588676
Author information:
Pietsch, Wolfgang
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