An integrative dynamic model of Colombian population distribution, based on the maximum entropy principle and matter, energy, and information flow

Human society has increased its capacity to exploit natural resources thanks to new technologies, which are one of the results of information exchange in the knowledge society. Many approaches to understanding the interactions between human society and natural systems have been developed in the last...

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Autores:
Cardona-Almeida, Cesar Antonio
Obregón, Nelson
Canales, Fausto
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
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oai:repositorio.cuc.edu.co:11323/5713
Acceso en línea:
https://hdl.handle.net/11323/5713
https://repositorio.cuc.edu.co/
Palabra clave:
Integrated modelling
Social-ecological systems
Maximum entropy principle
Energy and information
Human population distribution
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network_acronym_str RCUC2
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repository_id_str
dc.title.spa.fl_str_mv An integrative dynamic model of Colombian population distribution, based on the maximum entropy principle and matter, energy, and information flow
title An integrative dynamic model of Colombian population distribution, based on the maximum entropy principle and matter, energy, and information flow
spellingShingle An integrative dynamic model of Colombian population distribution, based on the maximum entropy principle and matter, energy, and information flow
Integrated modelling
Social-ecological systems
Maximum entropy principle
Energy and information
Human population distribution
title_short An integrative dynamic model of Colombian population distribution, based on the maximum entropy principle and matter, energy, and information flow
title_full An integrative dynamic model of Colombian population distribution, based on the maximum entropy principle and matter, energy, and information flow
title_fullStr An integrative dynamic model of Colombian population distribution, based on the maximum entropy principle and matter, energy, and information flow
title_full_unstemmed An integrative dynamic model of Colombian population distribution, based on the maximum entropy principle and matter, energy, and information flow
title_sort An integrative dynamic model of Colombian population distribution, based on the maximum entropy principle and matter, energy, and information flow
dc.creator.fl_str_mv Cardona-Almeida, Cesar Antonio
Obregón, Nelson
Canales, Fausto
dc.contributor.author.spa.fl_str_mv Cardona-Almeida, Cesar Antonio
Obregón, Nelson
Canales, Fausto
dc.subject.spa.fl_str_mv Integrated modelling
Social-ecological systems
Maximum entropy principle
Energy and information
Human population distribution
topic Integrated modelling
Social-ecological systems
Maximum entropy principle
Energy and information
Human population distribution
description Human society has increased its capacity to exploit natural resources thanks to new technologies, which are one of the results of information exchange in the knowledge society. Many approaches to understanding the interactions between human society and natural systems have been developed in the last decades, and some have included considerations about information. However, none of them has considered information as an active variable or flowing entity in the human–natural/social-ecological system, or, moreover, even as a driving force of their interactions. This paper explores these interactions in socio-ecological systems by briefly introducing a conceptual frame focused on the exchange of information, matter, and energy. The human population is presented as a convergence variable of these three physical entities, and a population distribution model for Colombia is developed based on the maximum entropy principle to integrate the balances of related variables as macro-state restrictions. The selected variables were electrical consumption, water demand, and higher education rates (energy, matter, and information). The final model includes statistical moments for previous population distributions. It is shown how population distribution can be predicted yearly by combining these variables, allowing future dynamics exploration. The implications of this model can contribute to bridging information sciences and sustainability studies.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-11-29T20:29:07Z
dc.date.available.none.fl_str_mv 2019-11-29T20:29:07Z
dc.date.issued.none.fl_str_mv 2019-11-29
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.content.spa.fl_str_mv Text
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dc.identifier.issn.spa.fl_str_mv 1099-4300
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 1099-4300
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/5713
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.spa.fl_str_mv https://doi.org/10.3390/e21121172
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spelling Cardona-Almeida, Cesar AntonioObregón, NelsonCanales, Fausto2019-11-29T20:29:07Z2019-11-29T20:29:07Z2019-11-291099-4300https://hdl.handle.net/11323/5713Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Human society has increased its capacity to exploit natural resources thanks to new technologies, which are one of the results of information exchange in the knowledge society. Many approaches to understanding the interactions between human society and natural systems have been developed in the last decades, and some have included considerations about information. However, none of them has considered information as an active variable or flowing entity in the human–natural/social-ecological system, or, moreover, even as a driving force of their interactions. This paper explores these interactions in socio-ecological systems by briefly introducing a conceptual frame focused on the exchange of information, matter, and energy. The human population is presented as a convergence variable of these three physical entities, and a population distribution model for Colombia is developed based on the maximum entropy principle to integrate the balances of related variables as macro-state restrictions. The selected variables were electrical consumption, water demand, and higher education rates (energy, matter, and information). The final model includes statistical moments for previous population distributions. It is shown how population distribution can be predicted yearly by combining these variables, allowing future dynamics exploration. The implications of this model can contribute to bridging information sciences and sustainability studies.Cardona-Almeida, Cesar Antonio-will be generated-orcid-0000-0002-7030-3782-600Obregón, NelsonCanales, Fausto-will be generated-orcid-0000-0002-6858-1855-600engEntropyhttps://doi.org/10.3390/e211211722. Freeman, C.; Louca, I.F.; Louca, F.; Louçã, F.; Iseg, F.L. As Time Goes by: From the Industrial Revolutions to the Information Revolution; Oxford University Press, Oxford, UK, 2001; ISBN 978-0-19-924107-1.3. Pahl-Wostl, C.; Craps, M.; Dewulf, A.; Mostert, E.; Tabara, D.; Taillieu, T. Social Learning and Water Resources Management. Ecol. Soc. 2007, 12. doi:10.5751/ES-02037-120205.4. Liu, B.; Yang, Q.; Xue, C.; Zhong, C.; Smit, B. Molecular simulation of hydrogen diffusion in interpenetrated metal–organic frameworks. Phys. Chem. Chem. Phys. 2008, 10, 3244.5. Pastor, J. Mathematical Ecology of Populations and Ecosystems; John wiley and Sons: Oxford, UK, 2008.6. Lischka, S.A.; Teel, T.L.; Johnson, H.E.; Reed, S.E.; Breck, S.; Carlos, A.D.; Crooks, K.R. A conceptual model for the integration of social and ecological information to understand human-wildlife interactions. Biol. Conserv. 2018, 225, 80–87.7. Dressel, S.; Ericsson, G.; Sandström, C. Mapping social-ecological systems to understand the challenges underlying wildlife management. Environ. Sci. Policy 2018, 84, 105–112.8. Binder, C.; Hinkel, J.; Bots, P.; Pahl-Wostl, C. Comparison of Frameworks for Analyzing Social-ecological Systems. Ecol. Soc. 2013, 18, 26.9. Resiliance Alliance Social-Ecological Systems. Available online: http://www.resalliance.org/index.php/index.php?id=1268&sr=1&type=pop (accessed on 20 October 2013).10. Stockholm Resilience Centre Resilience Dictionary. Available online: http://www.stockholmresilience.org/21/research/what-is-resilience/resilience-dictionary.html (accessed on 20 October 2013).11. Virapongse, A.; Brooks, S.; Metcalf, E.C.; Zedalis, M.; Gosz, J.; Kliskey, A.; Alessa, L. A social-ecological systems approach for environmental management. J. Environ. Manag. 2016, 178, 83–91.12. Hoole, A.; Berkes, F. Breaking down fences: Recoupling social–ecological systems for biodiversity conservation in Namibia. Geoforum 2010, 41, 304–317.13. Mitchell, M.; Lockwood, M.; Moore, S.A.; Clement, S. Scenario analysis for biodiversity conservation: A social–ecological system approach in the Australian Alps. J. Environ. Manag. 2015, 150, 69–80.14. Bair, L.S.; Yackulic, C.B.; Schmidt, J.C.; Perry, D.M.; Kirchhoff, C.J.; Chief, K.; Colombi, B.J. Incorporating social-ecological considerations into basin-wide responses to climate change in the Colorado River Basin. Curr. Opin. Environ. Sustain. 2019, 37, 14–19.15. Nguyen, V.M.; Lynch, A.J.; Young, N.; Cowx, I.G.; Beard, T.D.; Taylor, W.W.; Cooke, S.J. To manage inland fisheries is to manage at the social-ecological watershed scale. J. Environ. Manag. 2016, 181, 312–325.16. Vihervaara, P.; Franzese, P.P.; Buonocore, E. Information, energy, and eco-exergy as indicators of ecosystem complexity. Ecol. Model. 2019, 395, 23–27.17. Fischer, A.P. Forest landscapes as social-ecological systems and implications for management. Landsc. Urban Plan. 2018, 177, 138–147.18. Izquierdo, L.R.; Galán, J.M.; Santos, J.I. 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