A Fuzzy Logic Approach to Adjusting Monetary Policies in North African Countries

Abstract

The study explores the use of fuzzy logic as mathematical approach that handles uncertainty by allowing variables to belong to multiple categories with varying degrees of membership.in analyzing exchange rate fluctuations in Algeria and Tunisia. Using trapezoidal membership functions defined as Low, Medium, and High, the methodology applies fuzzy logic to historical exchange rate data, enabling partial membership across categories and offering a flexible analytical framework. In Algeria, where the exchange rate ranges from 45 to 150, results indicate high volatility, reflected in broader membership intervals (Low: 0.8 at 50, Medium: 0.9 at 75, High: 1.0 at 125). In contrast, Tunisia’s exchange rate, ranging from 0.3 to 2.5, shows a more stable environment, with narrower membership ranges (Low: 1.0 at 0.5, Medium: 0.8 at 1.5, High: 0.5 at 2). These findings demonstrate that fuzzy logic improves the interpretation of exchange rate dynamics, enhances forecasting accuracy, supports effective risk management, and strengthens policy formulation—providing a strategic tool for economic decision-making in both countries..