Download This Paper Open PDF in Browser

Knowledge and Truth in Data Science: Theory Without Theory?

30 Pages Posted: 28 Dec 2017  

Valeriano Diviacchi

Harvard Law School; New York University

Date Written: December 20, 2017

Abstract

Essay contemplation on whether Data Science with its correlation without explanation, in particular without need of cause and effect explanation, normatively ought to be the new scientific method. This essay argues it should. Not only is statistical correlation the next step in scientific progress for its epistemology necessary to deal with the huge amount of information and data that is now available and that will increase as Technological Society progresses, but it also provides a predictive and pragmatic methodology that avoids the illogic and paradoxes of cause and effect and induction. Furthermore, statistical correlation has a potential for allowing morality and ethics to logically and structurally avoid Hume's Guillotine.

Keywords: data science, correlation, scientific methodology, statistics, normative, induction, cause and effect

JEL Classification: Y1, C40

Suggested Citation

Diviacchi, Valeriano, Knowledge and Truth in Data Science: Theory Without Theory? (December 20, 2017). Available at SSRN: https://ssrn.com/abstract=3091356 or http://dx.doi.org/10.2139/ssrn.3091356

Valeriano Diviacchi (Contact Author)

Harvard Law School ( email )

1563 Massachusetts Avenue
Cambridge, MA 02138
United States

New York University ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
United States

Paper statistics

Downloads
2
Abstract Views
53