Modelling uncertainty in social–natural interactions

Ropero, Rosa F.; Rumí, Rafael; Aguilera, Pedro A.

doi:10.1016/j.envsoft.2014.07.008

Cited by 16 publications

(17 citation statements)

References 75 publications

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“…In fact, although around 40% of empirical studies reported both biophysical and sociological variables, only the half tried to develop tools to integrate both sets of variables. These efforts were mainly accomplished using geographic information systems, multivariate analysis, and mathematical models, such as multiple regression analysis [6,[40][41][42] or Bayesian networks [43][44][45]. Thus, there is room for developing SES studies by using analyses that combine both sets of variables at different spatial and temporal scales.…”

Section: The Matter Of Socio-ecological Systems: What Who How and mentioning

confidence: 99%

What do We Talk about When We Talk about Social-Ecological Systems? A Literature Review

et al. 2018

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In the last decade, probably in response to global changes and the environmental crisis, the use of the term "social-ecological system" (SES) in scientific literature has grown. This is certainly a sign that the need and importance of transdisciplinary research has been recognized. Here, we explore whether the use of the term is a buzzword or, rather, actually represents a key concept in the integration of social and ecological research. We compiled a database of publications (N = 1289) that mentioned SES in the title, keywords and abstract. Subsequently, we analyzed the authors' affiliations, type of work (conceptual, empirical or review), study site, prevailing human use, temporal and spatial scales of the analysis, kind of variables analyzed (socioeconomic or biophysical), and the method/s used to integrate them. We detected four time spans in the use of the term ). Our results suggest that SES is a widely invoked concept in the study of the interface between social and ecological systems. Most works show some common elements, such as the analysis of resilience, ecosystem services, sustainability, governance and adaptive management. However, the majority of studies do not study SES as a whole, integrating both social and ecological variables and their feedback loops. We consider SES as a concept still in construction in order to build a necessary framework for the integration of social and ecological sciences. For a robust evolution, we recommend that one focus on: (i) A conscious, discussed and agreed effort of scientists to conduct the transdisciplinary research needed to study SES; and (ii) the development of methodological tools for the true integration of social and ecological data.

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Section: The Matter Of Socio-ecological Systems: What Who How and mentioning

confidence: 99%

What do We Talk about When We Talk about Social-Ecological Systems? A Literature Review

et al. 2018

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“…One of the main advantages of BNs is that they provide not only a numeric prediction of the class variable but also its probability distribution, which allows several metrics to be calculated (i.e., mean, median, and probability of a certain range of values; Ropero et al, ). As Figure shows, the target variable percent fullness can be studied in detail, its probability distribution, mean, standard deviation, or even the probability of extreme (tail) values.…”

Section: Discussionmentioning

confidence: 99%

“…Another advantage of MTEs is that they are closed under restriction, marginalization, and combination, so standard BNs inference processes can be applied. Up to now, MTEs and Gaussian models are the only alternatives that have been applied in environmental sciences (Aguilera, Fernández, Reche, & Rumí, ; Maldonado, Aguilera, & Salmerón, ; Meineri, Dahlberg, & Hylander, ; Ropero, Rumí, & Aguilera, ).…”

Section: Methodsmentioning

confidence: 99%

“…From the water management point of view, it is often interesting to compute the probability that a reservoir reaches a certain level of percent fullness, both in the lowest and highest values. As an example, we compute the probability of values below 25% (left tail) and over 80% (right tail) of percent fullness (for a detailed explanation of how to compute the probability of a range of values, see Ropero et al, ).…”

Section: Methodsmentioning

confidence: 99%

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Applications of hybrid dynamic Bayesian networks to water reservoir management

et al. 2016

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Bayesian networks (BNs) have been widely applied in environmental modelling to predict the behavior of an ecosystem under conditions of change. However, this approximation doesn't take time into consideration. To solve this issue, an extension of BNs, the dynamic Bayesian networks (DBNs), has been developed in mathematics and computer science areas but has scarcely been applied in environmental modelling. This paper presents the application of DBN to water reservoir systems in Andalusia, Spain. The aim is to predict changes in the percent fullness of the reservoirs under the irregular rainfall patterns of Mediterranean watersheds. In comparison to static BNs, DBNs provide results that can be extrapolated to a particular time so that a climate change scenario can be studied in detail over time. Because results are expressed by density functions rather than unique values, several metrics are obtained from the results, including the probability of certain values. This allows the probability that water level in a reservoir reaches a certain level to be directly computed.

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“…Socio-ecological systems (SESs) constitute an outstanding example of complex systems, where multiple social and ecological components interact with each other in space and time [1,2]. SESs are complex adaptive systems whose interactions might change as a response to external events or endogenous changes [3,4]. As a consequence, the state of the SES evolves to a new one to adapt to these changes [5].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Uncertainty in Complex Socio-Ecological Networks

Maldonado

Morales

Aguilera

et al. 2020

Entropy

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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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Modelling uncertainty in social–natural interactions

Cited by 16 publications

References 75 publications

What do We Talk about When We Talk about Social-Ecological Systems? A Literature Review

What do We Talk about When We Talk about Social-Ecological Systems? A Literature Review

Applications of hybrid dynamic Bayesian networks to water reservoir management

Analyzing Uncertainty in Complex Socio-Ecological Networks

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