APPLICATION OF FUZZY LOGIC AND ARTIFICIAL INTELLIGENCE IN ECONOM-IC DECISION-MAKING PROCESSES

Abstract The application of fuzzy logic and artificial intelligence in economic decision-making processes has become increasingly relevant, particularly in conditions of uncertainty and incomplete information, where it contributes to more effective decision outcomes. Traditional methods demonstrate limitations when addressing the complexity of multi-criteria economic systems, conflicting objectives, and imprecise data. Fuzzy logic allows the incorporation of subjective assessments and linguistic variables into mathematical models, enhancing the flexibility and reliability of decision-making. This paper analyzes the combined application of fuzzy logic and artificial intelligence in optimizing resource allocation, evaluating alternatives, and supporting strategic planning processes. Empirical studies from recent decades indicate that these approaches improve decision quality, reduce associated risks, and facilitate the data-driven and adaptive implementation of economic strategies. At the same time, challenges such as computational complexity, algorithmic bias, and data accuracy remain pertinent issues. The study emphasizes the necessity of integrating expert knowledge and ethical principles into AI-driven decision-making frameworks. The findings suggest that the use of fuzzy logic and artificial intelligence enhances the transparency, agility, and reliability of economic decision-making, enabling organizations to adapt more efficiently to dynamic market conditions.