Using Fuzzy Cognitive Maps as a Decision Support System for Political Decisions: The Case of Turkey’s Integration into the European Union

Tsadiras, Athanasios; Kouskouvelis, Ilias

doi:10.1007/11573036_35

Cited by 13 publications

(2 citation statements)

References 7 publications

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“…Due to the flexibility and ability of FCM to represent human knowledge, FCM has been used for modelling numerous different ill-structured domains such as: political decisions and developments [53,56], socio-ecological systems [14,17,21,26,42,43,54], socio-economical systems [10], agriculture [27,44], medical decision-making [1,23,52], catastrophic natural events, e.g., earthquakes [50,51], and water and drought management [17][18][19].…”

Section: Fuzzy Cognitive Maps (Fcm)mentioning

confidence: 99%

A novel semi-quantitative Fuzzy Cognitive Map model for complex systems for addressing challenging participatory real life problems

Obiedat

Samarasinghe

2016

Applied Soft Computing

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Section: Fuzzy Cognitive Maps (Fcm)mentioning

confidence: 99%

A novel semi-quantitative Fuzzy Cognitive Map model for complex systems for addressing challenging participatory real life problems

Obiedat

Samarasinghe

2016

Applied Soft Computing

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“…FCM models are suitable and particularly useful for modeling and decision-making in the case of complex systems. They have been used in various application domains, e.g., for pattern recognition [2], in risk analysis and crisis management [3], as a decision support tool for political decisions [4], to model an undersea virtual world of dolphins, fish, and sharks [5], for sustainable socio-economic development planning [6], and to support the decision-making process for photovoltaic solar energy sector development [7].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Cognitive Maps Optimization for Decision Making and Prediction

2020

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Representing and analyzing the complexity of models constructed by data is a difficult and challenging task, hence the need for new, more effective techniques emerges, despite the numerous methodologies recently proposed in this field. In the present paper, the main idea is to systematically create a nested structure, based on a fuzzy cognitive map (FCM), in which each element/concept at a higher map level is decomposed into another FCM that provides a more detailed and precise representation of complex time series data. This nested structure is then optimized by applying evolutionary learning algorithms. Through the application of a dynamic optimization process, the whole nested structure based on FCMs is restructured in order to derive important relationships between map concepts at every nesting level as well as to determine the weights of these relationships on the basis of the available time series. This process allows discovering and describing hidden relationships among important map concepts. The paper proposes the application of the suggested nested approach for time series forecasting as well as for decision-making tasks regarding appliances’ energy consumption prediction.

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