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Unification of Epistemic and Ontic Concepts of Information, Probability, and Entropy, Using Cognizers-System Model

Information and probability are common words used in scientific investigations. However, information and probability both involve epistemic (subjective) and ontic (objective) interpretations under the same terms, which causes controversy within the concept of entropy in physics and biology. There is...

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Autor principal: Nakajima, Toshiyuki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514697/
https://www.ncbi.nlm.nih.gov/pubmed/33266931
http://dx.doi.org/10.3390/e21020216
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author Nakajima, Toshiyuki
author_facet Nakajima, Toshiyuki
author_sort Nakajima, Toshiyuki
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description Information and probability are common words used in scientific investigations. However, information and probability both involve epistemic (subjective) and ontic (objective) interpretations under the same terms, which causes controversy within the concept of entropy in physics and biology. There is another issue regarding the circularity between information (or data) and reality: The observation of reality produces phenomena (or events), whereas the reality is confirmed (or constituted) by phenomena. The ordinary concept of information presupposes reality as a source of information, whereas another type of information (known as it-from-bit) constitutes the reality from data (bits). In this paper, a monistic model, called the cognizers-system model (CS model), is employed to resolve these issues. In the CS model, observations (epistemic) and physical changes (ontic) are both unified as “cognition”, meaning a related state change. Information and probability, epistemic and ontic, are formalized and analyzed systematically using a common theoretical framework of the CS model or a related model. Based on the results, a perspective for resolving controversial issues of entropy originating from information and probability is presented.
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spelling pubmed-75146972020-11-09 Unification of Epistemic and Ontic Concepts of Information, Probability, and Entropy, Using Cognizers-System Model Nakajima, Toshiyuki Entropy (Basel) Article Information and probability are common words used in scientific investigations. However, information and probability both involve epistemic (subjective) and ontic (objective) interpretations under the same terms, which causes controversy within the concept of entropy in physics and biology. There is another issue regarding the circularity between information (or data) and reality: The observation of reality produces phenomena (or events), whereas the reality is confirmed (or constituted) by phenomena. The ordinary concept of information presupposes reality as a source of information, whereas another type of information (known as it-from-bit) constitutes the reality from data (bits). In this paper, a monistic model, called the cognizers-system model (CS model), is employed to resolve these issues. In the CS model, observations (epistemic) and physical changes (ontic) are both unified as “cognition”, meaning a related state change. Information and probability, epistemic and ontic, are formalized and analyzed systematically using a common theoretical framework of the CS model or a related model. Based on the results, a perspective for resolving controversial issues of entropy originating from information and probability is presented. MDPI 2019-02-24 /pmc/articles/PMC7514697/ /pubmed/33266931 http://dx.doi.org/10.3390/e21020216 Text en © 2019 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nakajima, Toshiyuki
Unification of Epistemic and Ontic Concepts of Information, Probability, and Entropy, Using Cognizers-System Model
title Unification of Epistemic and Ontic Concepts of Information, Probability, and Entropy, Using Cognizers-System Model
title_full Unification of Epistemic and Ontic Concepts of Information, Probability, and Entropy, Using Cognizers-System Model
title_fullStr Unification of Epistemic and Ontic Concepts of Information, Probability, and Entropy, Using Cognizers-System Model
title_full_unstemmed Unification of Epistemic and Ontic Concepts of Information, Probability, and Entropy, Using Cognizers-System Model
title_short Unification of Epistemic and Ontic Concepts of Information, Probability, and Entropy, Using Cognizers-System Model
title_sort unification of epistemic and ontic concepts of information, probability, and entropy, using cognizers-system model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514697/
https://www.ncbi.nlm.nih.gov/pubmed/33266931
http://dx.doi.org/10.3390/e21020216
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