A model for multiple appearances based on Williamson's GCEL

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Human epistemic subjects cannot but employ imperfect and limited tools to gain knowledge. Even in the seemingly simple business of acquiring knowledge of the value of a physical quantity, what the instrument reads or perception tells more often that not does not correspond to real value. However, even though both our perceptual apparatus and measuring instruments are sensible to background noise, under certain conditions, collecting more information of the same quantity using the same tools leads to an improvement of the subject's epistemic condition. The aim of this paper is to formalize this intuition employing a model which extends Williamson (2013) on the arising of Gettier cases in epistemic logic. From a model where each world is univocally represented by the pair r, a defined by the real value and the apparent one, I will be considering a scenario wherein the subject has collected n appearances of the real parameter, so that each world is identified by a n + 1-tuple. On the assumptions that the n data are all independent and regarded by the subject as equally reliable, a plausible model should be able to describe three phenomena. First of all, the more coherent and closer to represent the real value the set of n appearances is, the more knowledge of the real value is gained. Secondly, the more the subject goes on acquiring "good" data, the more she knows. Thirdly, the world where the n apparent values and the real one match is the best world for the subject. Following Williamson, I shall put forth a system for multiple appearances in epistemic logic that aims at modeling these phenomena by describing the interval within which the real value is known to lie.
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Archival date: 2020-08-12
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