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Analyzing Uncertainty in Complex Socio-Ecological Networks

Socio-ecological systems are recognized as complex adaptive systems whose multiple interactions might change as a response to external or internal changes. Due to its complexity, the behavior of the system is often uncertain. Bayesian networks provide a sound approach for handling complex domains en...

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Autores principales: Maldonado, Ana D., Morales, María, Aguilera, Pedro A., Salmerón, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516428/
https://www.ncbi.nlm.nih.gov/pubmed/33285898
http://dx.doi.org/10.3390/e22010123
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author Maldonado, Ana D.
Morales, María
Aguilera, Pedro A.
Salmerón, Antonio
author_facet Maldonado, Ana D.
Morales, María
Aguilera, Pedro A.
Salmerón, Antonio
author_sort Maldonado, Ana D.
collection PubMed
description Socio-ecological systems are recognized as complex adaptive systems whose multiple interactions might change as a response to external or internal changes. Due to its complexity, the behavior of the system is often uncertain. Bayesian networks provide a sound approach for handling complex domains endowed with uncertainty. The aim of this paper is to analyze the impact of the Bayesian network structure on the uncertainty of the model, expressed as the Shannon entropy. In particular, three strategies for model structure have been followed: naive Bayes (NB), tree augmented network (TAN) and network with unrestricted structure (GSS). Using these network structures, two experiments are carried out: (1) the impact of the Bayesian network structure on the entropy of the model is assessed and (2) the entropy of the posterior distribution of the class variable obtained from the different structures is compared. The results show that GSS constantly outperforms both NB and TAN when it comes to evaluating the uncertainty of the entire model. On the other hand, NB and TAN yielded lower entropy values of the posterior distribution of the class variable, which makes them preferable when the goal is to carry out predictions.
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spelling pubmed-75164282020-11-09 Analyzing Uncertainty in Complex Socio-Ecological Networks Maldonado, Ana D. Morales, María Aguilera, Pedro A. Salmerón, Antonio Entropy (Basel) Article Socio-ecological systems are recognized as complex adaptive systems whose multiple interactions might change as a response to external or internal changes. Due to its complexity, the behavior of the system is often uncertain. Bayesian networks provide a sound approach for handling complex domains endowed with uncertainty. The aim of this paper is to analyze the impact of the Bayesian network structure on the uncertainty of the model, expressed as the Shannon entropy. In particular, three strategies for model structure have been followed: naive Bayes (NB), tree augmented network (TAN) and network with unrestricted structure (GSS). Using these network structures, two experiments are carried out: (1) the impact of the Bayesian network structure on the entropy of the model is assessed and (2) the entropy of the posterior distribution of the class variable obtained from the different structures is compared. The results show that GSS constantly outperforms both NB and TAN when it comes to evaluating the uncertainty of the entire model. On the other hand, NB and TAN yielded lower entropy values of the posterior distribution of the class variable, which makes them preferable when the goal is to carry out predictions. MDPI 2020-01-19 /pmc/articles/PMC7516428/ /pubmed/33285898 http://dx.doi.org/10.3390/e22010123 Text en © 2020 by the authors. 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
Maldonado, Ana D.
Morales, María
Aguilera, Pedro A.
Salmerón, Antonio
Analyzing Uncertainty in Complex Socio-Ecological Networks
title Analyzing Uncertainty in Complex Socio-Ecological Networks
title_full Analyzing Uncertainty in Complex Socio-Ecological Networks
title_fullStr Analyzing Uncertainty in Complex Socio-Ecological Networks
title_full_unstemmed Analyzing Uncertainty in Complex Socio-Ecological Networks
title_short Analyzing Uncertainty in Complex Socio-Ecological Networks
title_sort analyzing uncertainty in complex socio-ecological networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516428/
https://www.ncbi.nlm.nih.gov/pubmed/33285898
http://dx.doi.org/10.3390/e22010123
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