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Decision makers, whether they are social scientists, politicians or economists, usually face serious difficulties when trying to model significant, real-world dynamic systems. Such systems are composed of a number of dynamic qualitative concepts interrelated in complex ways, usually including feedback links that propagate influences in complicated chains. Axelrod work on Cognitive Maps (CMs) introduced a way to represent real-world qualitative dynamic systems, and several methods and tools have been developed to analyze the structure of CMs. However, complete, efficient and practical mechanisms to analyze and predict the evolution of data in CMs were not available for years due to several reasons. System Dynamics tools like those developed by J. W. Forrester could have provided the solution, but since in CMs numerical data may be uncertain or hard to come by, and the formulation of a mathematical model may be difficult, costly or even impossible due to their qualitative and uncertain nature, then efforts to introduce knowledge on these systems should rely on natural language arguments in the absence of formal models. Fuzzy Cognitive Maps (FCM), as introduced by Kosko, were developed as a qualitative alternative approach to system dynamics. However, FCMare Causal Maps (a subset of Cognitive Maps that only allow basic symmetric and monotonic causal relations), and in most applications do not explore usual Fuzzy capabilities. They do not share the properties of other fuzzy systems and the causal maps usually result in quantitative matrixes without any qualitative knowledge. This talk introduces Rule Based Fuzzy Cognitive Maps (RB-FCM), a new approach to model and simulate real world qualitative dynamic systems (social, economic, political, etc.) while avoiding the limitations of the above alternatives.

DOI: 10.1007/978-3-642-24672-2_16

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