Big Data

Erscheinungsjahr 2021
ISBN: 978-1-108-70669-8
Verlag: Cambridge University Press

Buch, Englisch, 75 Seiten, Format (B × H): 147 mm x 225 mm, Gewicht: 136 g

Reihe: Elements in the Philosophy of Science

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.

Autoren/Hrsg.

Pietsch, Wolfgang

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
• Interdisziplinäres Wissenschaften Wissenschaften: Allgemeines Geschichte der Naturwissenschaften, Formalen Wissenschaften & Technik
• Geisteswissenschaften Philosophie Wissenschaftstheorie, Wissenschaftsphilosophie
• Geisteswissenschaften Geschichtswissenschaft Geschichtliche Themen Wissenschafts- und Universitätsgeschichte

Inhaltsverzeichnis

1. Introduction; 2. Defining Big Data; 3. Inductivism; 4. Machine Learning as Variational Induction; 5. Correlation and Causation; 6. The Role of Theory; 7. Novel Scientific Methodology?; 8. Conclusion.