A framework for static and dynamic analysis of multi-layer fuzzy cognitive maps

A methodology for using a multilevel perspective framework to analyze complex systems

Misalignment between the different levels of complex societal systems (e.g. socio-technical, socio-ecological) are often a barrier to transitioning these systems toward more sustainable behavior. One way to understand alignment is through the Multilevel Perspective Theory of sustainable transitions in sociotechnical systems (MLP) which posits that when a regime (the current “ways of doing” something) exhibits behavior that negatively impacts its surrounding economic, social and environmental landscape, that landscape will exert pressure on the regime to change. This in turn opens the window of opportunity for niche developments such as novel technology adoption or policy changes. Therefore, understanding the relationships between the landscape, the regime and emerging niches can help align these different levels of perspective to facilitate a successful transition toward more sustainable practices. This paper describes a methodology for mapping complex systems such as agricultural systems based on MLP theory, and incorporates a fourth “user” level (e.g. the farm) into this multilevel framework (MLP+ F). The methodology is illustrated with a case study example of North Carolina’s lagoon and sprayfield swine waste management regime. By integrating well-established techniques for data collection and analysis, the case study engages a diverse panel of local experts through a modified Delphi approach to identify the relevant concepts and their cause-effect relationships for the current regime. These concepts and relationships are then categorized and organized into a multilevel, multi-perspective conceptual model using fuzzy cognitive maps. The conceptual model identifies how stakeholder groups align in their perception of the system, and provides a foundation for future qualitative, semi-quantitative, and visual analyses of the system and how it can be changed through alternative scenarios. Although this methodology was initially developed for agricultural applications, it has potential for application to other complex societal issues where understanding stakeholder alignment and the potential for sustainability transitions through change are needed.

Advanced fuzzy cognitive maps: state-space and rule-based methodology for coronary artery disease detection

According to the World Health Organization, 50% of deaths in European Union are caused by Cardiovascular Diseases (CVD), while 80% of premature heart diseases and strokes can be prevented. In this study, a Computer-Aided Diagnostic model for a precise diagnosis of Coronary Artery Disease (CAD) is proposed. The methodology is based on State Space Advanced Fuzzy Cognitive Maps (AFCMs), an evolution of the traditional Fuzzy Cognitive Maps. Also, a rule-based mechanism is incorporated, to further increase the knowledge of the proposed system and the interpretability of the decision mechanism. The proposed method is evaluated utilizing a CAD dataset from the Department of Nuclear Medicine of the University Hospital of Patras, in Greece. Several experiments are conducted to define the optimal parameters of the proposed AFCM. Furthermore, the proposed AFCM is compared with the traditional FCM approach and the literature. The experiments highlight the effectiveness of the AFCM approach, obtaining 85.47% accuracy in CAD diagnosis, showing an improvement of +7% over the traditional approach. It is demonstrated that the AFCM approach in developing Fuzzy Cognitive Maps outperforms the conventional approach, while it constitutes a reliable method for the diagnosis of Coronary Artery Disease.

Reprint of: Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts

The fuzzy cognitive map (FCM) is an effective tool for modeling dynamic decision support systems. It describes the analyzed phenomenon in the form of key concepts and the causal connections between them. The main aspects of the building of the FCM model are: concepts selection, determining the output concepts, criterion selection, and determining the relationships between concepts. It is usually based on expert knowledge. The main goal of the paper is to define the optimal in some sense FCM structure through the introduction of the notion of output concepts and minimizing the number of concepts and connections between them. The proposed approach allows for: (1) the selection of key concepts based on graph theory metrics and determining the connections between them; (2) the determination of the criterion of learning based on output concepts and fitting the learning process to the analyzed problem. A simulation analysis was done with the use of synthetic and real-life data. Experiments confirm that the proposed approach improves the learning process compared to the standard approaches.

Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts

The fuzzy cognitive map (FCM) is an effective tool for modeling dynamic decision support systems. It describes the analyzed phenomenon in the form of key concepts and the causal connections between them. The main aspects of the building of the FCM model are: concepts selection, determining the output concepts, criterion selection, and determining the relationships between concepts. It is usually based on expert knowledge. The main goal of the paper is to define the optimal in some sense FCM structure through the introduction of the notion of output concepts and minimizing the number of concepts and connections between them. The proposed approach allows for: (1) the selection of key concepts based on graph theory metrics and determining the connections between them; (2) the determination of the criterion of learning based on output concepts and fitting the learning process to the analyzed problem. A simulation analysis was done with the use of synthetic and real-life data. Experiments confirm that the proposed approach improves the learning process compared to the standard approaches.