Abstract—Economic models are complex dynamic systems with a lot of uncertainties and fuzzy data. Conventional modeling approaches using well known methods and techniques cannot provide realistic and satisfactory answers to today’s challenging economic problems. Qualitative modeling using fuzzy logic and intelligent system theories can be used to model macroeconomic models. Fuzzy Cognitive maps (FCM) is a new method been used to model the dynamic behavior of complex systems. For the first time FCMs and the Mamdani Model of Intelligent control is used to model macroeconomic models. This new model is referred as the Mamdani Rule-Based Fuzzy Cognitive Map (MBFCM) and provides the academic and research community with a new promising integrated advanced computational model. A new economic model is developed for a qualitative approach to Macroeconomic modeling. Fuzzy Controllers for such models are designed. Simulation results for an economic scenario are provided and extensively discussed. Keywords—Macroeconomic Models, Mamdani Rule Based- FCMs(MBFCMs), Qualitative and Dynamics System, Simulation. I. INTRODUCTION MACROECONOMIC model is an analytical tool designed to describe the dynamic behavior of the economy of a region or a country. It is a means of collating research on the economy in a systematic and policy-relevant way, and depends on the availability of such research. The goal of a macroeconomic model is to replicate the main mechanisms of an entire economic system, which may consist of a region, of a country or a union of countries. The only requirement is that the entity being modeled is large enough to display the distinctive properties that are the thematic area of macroeconomics. Today more than ever before given the world economic crisis and the peripheral economic inequalities, there is the need to search and develop new models and methodologies in order to study economy. It is needed to find new solutions to our everyday economic problems. Today most modeling approaches cannot always provide realistic and acceptable solutions. The economic environment in the whole world is complex dynamic and uncertain. Macroeconomic systems are complex and fuzzy [1]. Decision makers, especially economists, usually face serious difficulties when approaching Spiros Mazarakis, George Matzavinos, and Peter P. Groumpos are with the Laboratory for Automation and Robotics, Department of Electrical and Computer Engineering, University of Patras, Rion 26500, Greece (phone:+30 61 997293; fax. +30 61 997309; e-mail: spiros_mazarakis@hotmail.com, gematzab@windowslive.com, groumpos@ece.upatras.gr). significant, real-world dynamic systems. Fuzzy Cognitive Maps (FCMs), as introduced by Kosko [3], were developed as a qualitative alternative approach to model dynamics of complex systems. FCMs are Causal Maps (a subset of Cognitive Maps that only allows basic symmetric and monotonic causal relations). In most applications, a FCM is a man-trained Neural Network that is not Fuzzy in a traditional sense and does not explore usual Fuzzy capabilities. They do not share the properties of other fuzzy systems and the causal maps end up being quantitative matrices without any qualitative knowledge. Even then the FCMs have problems to provide the necessary environment for expressing the knowledge of a system. To anticipate solutions to these problems new models and methods are needed and improved FCM models are required. In this paper a new computational method that is called Madmani Based Fuzzy Cognitive Maps(MBFCMs) is developed. The new method is used for modeling and the simulation of a macroeconomic model. Mamdani controllers are designed. The method is applied on a process simulation and general on the control problem in the field of economy, with promising results. The paper is organized as follows: The overview of Fuzzy Cognitive Maps and Rule-Based is represent in Section II, the new method Mamdani Based-FCM is presented in Section III. In Section IV the Construction of a qualitative Macroeconomic Model is presented. In Section V the simulation of an economic scenario is described and the simulation results are reported and extensively discussed. The Section VI consists of Conclusion and Future Research. II. OVERVIEW OF FUZZY COGNITIVE MAPS AND MAMDANI RULE-BASED Overview of Fuzzy Cognitive Maps FCMs have been introduced by Kosko in 1986 [3] assigned directed graphs for representing causal reasoning and computational inference processing, exploiting a symbolic representation for the description and modeling of a system. Concepts are utilized to represent different aspects of the system, as well as, their behavior. The dynamics of the system are implied by the interaction of concepts. FCM structures are used to represent both qualitative and quantitative data. The construction of an FCM requires the input of human experience and knowledge on the system under consideration. Thus, FCMs integrate the accumulated experience and A Spiros Mazarakis, George Matzavinos, and Peter P. Groumpos Simulating and Forecasting Qualitative Marcoeconomic Models using Rule-Based Fuzzy Cognitive Maps World Academy of Science, Engineering and Technology International Journal of Economics and Management Engineering Vol:7, No:1, 2013 147 International Scholarly and Scientific Research & Innovation 7(1) 2013 ISNI:0000000091950263 Open Science Index, Economics and Management Engineering Vol:7, No:1, 2013 publications.waset.org/15466/pdf