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Entropy vs. human ontogeny

The Shannon information measure; a reliable general indicator of human health

Bibliographic Data

ID11936063
AuthorsRicardo Rodríguez (0000-0003-2752-005X, Independent Researcher, SC de Tenerife, Spain), Rodrigo Riera (0000-0003-1264-1625, Universidad Católica de la Santísima Concepción, corresponding author), Israel Reyes (Fisionatura Tenerife Co., SC de Tenerife, Spain), I Brito Reyes (Observatorio de la Inmigración de Tenerife), Juan D Delgado (0000-0001-5378-138X, Universidad Pablo de Olavide)
Year2019
Volume45
Issue2
Pages186-206
Publication date2019-12-13
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueInterdisciplinary Science Reviews (JOURNAL)
Journal identifiersISSN: 0308-0188 • E-ISSN: 1743-2790
PublisherTaylor & Francis (PUBLISHER • GB)
DOI10.1080/03080188.2019.1696026
OpenAlexW2995349888
LanguageEN
References cited103

A measure of health based on exact sciences is not considered by social sciences to evaluate human development. We propose the H measure of information of Shannon ([1948]. “A Mathematical Theory of Communication.” The Bell Systems Technical Journal 27 (3): 379–423, 623–656), renamed as H cto, as a general indicator of human health which can be subsequently aggregated at any statistical level. Several advantages of H cto are explained: (i) connections with thermodynamics and information theory; (ii) combination of physical and statistical elements; (iii) measurements of entropy and/or information depending on the reference point, and (iv) a collection of examples based on the scientific literature that supports the application of H cto. Simplified graphical models of the ontogeny of health condition are also shown. The empirical feasibility of using H cto is also analysed, by concluding that it could be a tangible reality in the near future

Binary entropy function · Data mining · Data science · Econometrics · Entropy (arrow of time · Human health · Information diagram · Information Theory · Maximum entropy thermodynamics · Measure (data warehouse · Physics · Principle of maximum entropy · Statistical physics · Statistics · Thermodynamics · Advanced Thermodynamics and Statistical Mechanics · Complex Systems and Decision Making · Computer Science · Mathematics · Medicine · Sustainability and Ecological Systems Analysis · Artificial Intelligence

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