Knowledge, Noise, and Curve-Fitting: A methodological argument for JTB?

In Rodrigo Borges, Claudio de Almeida & Peter David Klein (eds.), Explaining Knowledge: New Essays on the Gettier Problem. Oxford, United Kingdom: Oxford University Press (2017)
  Copy   BIBTEX

Abstract

The developing body of empirical work on the "Gettier effect" indicates that, in general, the presence of a Gettier-type structure in a case makes participants less likely to attribute knowledge in that case. But is that a sufficient reason to diverge from a JTB theory of knowledge? I argue that considerations of good model selection, and worries about noise and overfitting, should lead us to consider that a live, open question. The Gettier effect is perhaps so transient, and so sensitive to other, epistemologically-inappropriate factors, that it raises the question of whether it ought to be counted as something to include in our theories -- or as a piece of noise to be excluded from them.

Author's Profile

Jonathan Weinberg
University of Arizona

Analytics

Added to PP
2016-05-03

Downloads
485 (#32,990)

6 months
95 (#40,788)

Historical graph of downloads since first upload
This graph includes both downloads from PhilArchive and clicks on external links on PhilPapers.
How can I increase my downloads?