Using Fuzzy Cognitive Maps to predict the economic sustainability of Jordan Social Security

Abstract

Fuzzy Cognitive Maps are emerging as an important new tool in economic modeling. This study investigates the use of fuzzy cognitive maps with their learning algorithms, based on genetic algorithms, for the purposes of economic prediction. The case study data are extracted from the Jordanian social sociality revenues and expanse for the last 120 months; The Real-Code genetic algorithm and structure optimization algorithm were chosen for their ability to select the most significant relationships between the concepts and to predict future development of the Jordanian social sociality revenues and expenses. Furthermore, fuzzy cognitive maps are able to calculate prediction errors accurately. The study shows that fuzzy cognitive maps models clearly predict the future of a complex financial system with incoming and outgoing flows. Consequently, this research confirms the benefits of fuzzy cognitive maps applications as a tool for scholarly researchers, economists and policy makers.