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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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2020-01, Vol.22 (1), p.123
Main Authors: Maldonado, Ana D., Morales, María, Aguilera, Pedro A., Salmerón, Antonio
Format: Article
Language:English
Subjects:
Adaptive systems
Bayesian analysis
bayesian networks
Classification
Complexity
Decision making
Ecosystems
Entropy
Entropy (Information theory)
Environmental impact
Experiments
Impact analysis
Probability distribution
Random variables
socio-ecological system
Uncertainty analysis
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Summary: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.
ISSN:1099-4300
1099-4300
DOI:10.3390/e22010123