Approaches to Goal Setting in the Development of Intelligent Decision-Making Support Systems

Key words: goal decomposition, process automation, goal setting, data quality, external environment identification The article explores the design and implementation of intelligent decision-making support systems (IDMSS) for managing complex systems that require rapid, accurate decisions under uncertainty. It emphasizes the need for effective tools to enhance decision-making and formalize IDMSS development stages. These systems play a critical role in modern management by providing data analysis and scenario forecasting. To streamline IDMSS development, the ASSA methodology (Automation of Stages of System Analysis) is proposed, enabling automation of key system design stages. This work focuses on automating the “Goal Setting” stage, describing sub-stages and steps to refine goal formulation, decomposition, and criteria evaluation, improving efficiency and precision. Additionally, the integration of the “LOTOS” algorithm into IDMSS enhances decision-making accuracy. This algorithm effectively classifies multidimensional data, identifying regions with similar social and economic characteristics, aiding in the development of targeted strategies to improve quality of life. “LOTOS” enables deeper data analysis and better scenario forecasting. Expanding these methods will make IDMSS more adaptive and predictive in unstable, uncertain environments, unlocking new possibilities for effective management of complex systems