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Open AccessArticle

Paradigms of Cognition

Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen, Denmark
This paper is an extended version of our paper published in the 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Ghent, Belgium, 10–15 July 2016.
Entropy 2017, 19(4), 143; https://doi.org/10.3390/e19040143
Received: 19 December 2016 / Revised: 23 February 2017 / Accepted: 10 March 2017 / Published: 27 March 2017
(This article belongs to the Special Issue Selected Papers from MaxEnt 2016)
An abstract, quantitative theory which connects elements of information —key ingredients in the cognitive proces—is developed. Seemingly unrelated results are thereby unified. As an indication of this, consider results in classical probabilistic information theory involving information projections and so-called Pythagorean inequalities. This has a certain resemblance to classical results in geometry bearing Pythagoras’ name. By appealing to the abstract theory presented here, you have a common point of reference for these results. In fact, the new theory provides a general framework for the treatment of a multitude of global optimization problems across a range of disciplines such as geometry, statistics and statistical physics. Several applications are given, among them an “explanation” of Tsallis entropy is suggested. For this, as well as for the general development of the abstract underlying theory, emphasis is placed on interpretations and associated philosophical considerations. Technically, game theory is the key tool. View Full-Text
Keywords: entropy; divergence; redundancy; information triples; proper effort functions; fundamental inequality; Jensen-Shannon divergence; core; Bregman construction; Tsallis entropy entropy; divergence; redundancy; information triples; proper effort functions; fundamental inequality; Jensen-Shannon divergence; core; Bregman construction; Tsallis entropy
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Topsøe, F. Paradigms of Cognition. Entropy 2017, 19, 143.

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