Improved behavioral analysis of fuzzy cognitive map models

Fuzzy Cognitive Maps (FCMs) are widely applied for describing the major components of complex systems and their interconnections. The popularity of FCMs is mostly based on their simple system representation, easy model creation and usage, and its decision support capabilities. The preferable way of model construction is based on historical, measured data of the investigated system and a suitable learning technique. Such data are not always available, however. In these cases experts have to define the strength and direction of causal connections among the components of the system, and their decisions are unavoidably affected by more or less subjective elements. Unfortunately, even a small change in the estimated strength may lead to significantly different simulation outcome, which could pose significant decision risks. Therefore, the preliminary exploration of model ‘sensitivity’ to subtle weight modifications is very important to decision makers. This way their attention can be attracted to possible problems. This paper deals with the advanced version of a behavioral analysis. Based on the experiences of the authors, their method is further improved to generate more life-like, slightly modified model versions based on the original one suggested by experts. The details of the method is described, its application and the results are presented by an example of a banking application. The combination of Pareto-fronts and Bacterial Evolutionary Algorithm is a novelty of the approach. © Springer International Publishing AG, part of Springer Nature 2018.Peer reviewe

An improved fuzzy cognitive mapping method

International Conference on Intelligent and Fuzzy Systems, INFUS 2019 -- 23 July 2019 through 25 July 2019 -- -- 228529Fuzzy Cognitive Mapping (FCM) models the evolution of a system over time. However, FCM tends to produce the same steady state values under different initial conditions. To address this issue, this study suggests an improved FCM method that integrates the reachability matrix of the classical Cognitive Mapping method into FCM. The proposed method produces more distinctive and representative steady state values by appropriately incorporating the initial conditions. The applicability and effectiveness of the improved method are shown by an example. © 2020, Springer Nature Switzerland AG

The use of fuzzy cognitive maps for evaluating the reuse project of military barracks in northern Italy

Cities are complex systems and their changing are continuous. The evolution strictly depends on the relationships among the different aspects which compose the same structure, such as social, economic, political, environmental, historical aspects. The consideration of the complexity, the heterogeneity and reciprocal influences of these different elements becomes fundamental in urban regeneration projects that are characterized by many interconnected elements. Starting from a real project in the city of Pinerolo (Italy), the paper aims to investigate the potentiality of Fuzzy Cognitive Maps (FCMs) to represent the complexity of urban transformation processes, paying particular attention to the possibility of analyzing different scenarios simulatio

Exploring the resilience of urban systems using fuzzy cognitive maps

In the context of cites, a very innovative approach refers to the theory of urban resilience, which is represented by the ability of an urban system to absorb, adapt and respond to stresses and strains, including issues related to sustainability, governance and economic development. The paper aims at exploring the problems related to urban resilience, with specific attention to the use of Fuzzy Cognitive Maps (FCMs) which constitute a recent approach for representing complex systems and for supporting scenario planning and strategic decision making. Starting from a real case related to the regeneration program of the city of Collegno (Italy), the paper illustrates the application of the FCM method for modeling urban resilience dynamics and for exploring future scenarios of transformatio