Machine Epistemology and Big Data

Authored by: Gregory Wheeler

The Routledge Companion to Philosophy of Social Science

Print publication date:  December  2016
Online publication date:  December  2016

Print ISBN: 9781138825758
eBook ISBN: 9781315410098
Adobe ISBN: 9781315410081

10.4324/9781315410098.ch27

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Abstract

Here is a portrait of experimental science. A question arises, a hypothesis is proposed, experiments are devised, then performed, and a judgment is made on how well some or another implication of that hypothesis, supposing it were true, accords with the outcomes. Such was Charles Sanders Peirce’s view of experimental inquiry at the end of the nineteenth century. 1 By the close of the twentieth, a spectacular store of methods were on hand to quantify the uncertainty an experimentalist confronts, along with a logic, broadly speaking, to assess its consequences. Just as the differential calculus swept to the margins ancient bewilderment over change and how to reason about quantities that change, so have the triumphs of modern statistics pushed aside Cartesian paralysis over error and how to reason with corrigible, uncertain quantities. It is not so much that Leibniz and Newton answered Parmenides, or that Peirce set the course for Pearson and Fisher to refute the academic skeptics, but rather that in each case a genuine obstacle to inquiry was plucked from confusion and paradox and a clear way to reason around those obstacles was shown. For those who wonder what philosophical progress looks like, look no further.

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