Abstract:
This study develops a conceptual algorithm for the strategic management of labor market adaptation, viewed as a complex socio-economic system undergoing non-linear digital transformation. A systemic approach is implemented to synthesize a decision-support framework that bridges the gap between autonomous AI evolution and human capital management. The author applied a modified Bellman-Zadeh decision-making scheme, which allows for integrating additional objectives and constraints into a single weighted aggregation model. The developed model integrates conflicting systemic parameters-including labor productivity as a performance metric, social stability as a system constraint, and budget availability as a resource limit-into a unified analytical framework. The findings confirm the systemic hypothesis that a synergistic configuration of human-machine interaction yields higher global efficiency than isolated automation or rigid administrative restrictions. The internal consistency of the expert-knowledge base is mathematically verified through a consistency ratio analysis, ensuring the structural integrity of the weight coefficients.The scientific novelty lies in the transition from static planning to an adaptive modeling paradigm. The study proposes a roadmap for the systemic expansion of the algorithm through the integration of dynamic feedback loops, such as personnel psychological readiness and tax-induced investment variability in the IT sector. This architectural flexibility allows the model to maintain high predictive accuracy amidst the stochastic fluctuations of Ukraine's digital economy. The proposed framework serves as a scalable computational foundation for optimizing the evolution of labor market structures in the era of pervasive artificial intelligence
