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Information 2017, 8(2), 61; doi:10.3390/info8020061

Information and Inference

Capgemini UK, Forge End, Woking, Surrey GU21 6DB, UK
Academic Editor: Willy Susilo
Received: 16 April 2017 / Revised: 21 May 2017 / Accepted: 22 May 2017 / Published: 27 May 2017
(This article belongs to the Section Information Theory and Methodology)
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Abstract

Inference is expressed using information and is therefore subject to the limitations of information. The conventions that determine the reliability of inference have developed in information ecosystems under the influence of a range of selection pressures. These conventions embed limitations in information measures like quality, pace and friction caused by selection trade-offs. Some selection pressures improve the reliability of inference; others diminish it by reinforcing the limitations of the conventions. This paper shows how to apply these ideas to inference in order to analyse the limitations; the analysis is applied to various theories of inference including examples from the philosophies of science and mathematics as well as machine learning. The analysis highlights the limitations of these theories and how different, seemingly competing, ideas about inference can relate to each other. View Full-Text
Keywords: information; philosophy of science; inference; induction; information quality; information friction; machine learning information; philosophy of science; inference; induction; information quality; information friction; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Walton, P. Information and Inference. Information 2017, 8, 61.

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