Simulation and forecasting of international trade dynamics using non-linear mathematical models and fuzzy logic techniques

The authors describe paper non-linear mathematical models, from dynamical systems theory, that can be used to study the dynamics of international trade. Mathematical models of international trade (IT), between three or more countries, can show very complicated dynamics in time (with the possible occurrence of behavior known as chaos). The simulation of these models is critical in understanding the behavior of the relevant financial and economical variables for the problem of IT. Also, performing the simulations for different parameter values of the models will enable the prediction of future IT. The problem of modelling and simulation of IT has been solved in this paper by using artificial intelligence (AI) techniques. An intelligent system for automated modelling and simulation of IT has been developed to obtain the “best” mathematical model for a particular situation and then to obtain the “best” simulations for the model. The use of fuzzy logic techniques enables automated modelling of IT using as input real data (time series) for this problem and the use of expert systems technology enables the selection of the appropriate parameter values for the simulation, to obtain all the dynamic behaviors of the corresponding mathematical model. The importance of modelling, simulation and forecasting of IT can be measured if one considers that one of the goals for a specific country is to find the optimum benefit from its international trade with other countries