Development of a Mathematical Model for Decision-Making in Organizational Systems

The article presents a mathematical model and algorithmic support for a decision support system (DSS) for software development in the field of organizational systems. The focus is on integrating artificial intelligence methods, optimization, and risk analysis. A multi-level architecture has been developed, including modules for requirements collection, risk assessment, process modeling, and decision optimization. Modified methods such as adaptive Monte Carlo with ML-weighting and reinforcement learning for dynamic optimization are proposed. Testing on simulated and real cases showed a 30% reduction in risks, 20% in costs, and 25% acceleration in product launch to the market. The model complies with the principles of general systems theory and outperforms analogs in efficiency and adaptability.