Modeling regional innovation system development scenarios using intelligent systems

the objective of the study is to develop scenario models for forecasting the dynamics of a regional innovation system, with an emphasis on the use of intelligent technologies, as well as to identify patterns and principles that determine the specific functioning of the innovation environment. Methods: the study is based on a critical analysis of scientific publications, an assessment of existing methods for forecasting the development of a regional innovation system, and the use of strategic tools for analyzing the external and internal environments (SWOT, PEST). A sequence of forecasting stages has been developed. Results: a structured forecasting model has been formulated, supplemented by operational elements reflecting the functional specifics of the regional innovation system. The importance of an integrated approach, which involves the synthesis of objective and subjective factors in constructing forecasting models, is substantiated. This ensures the sustainability and practical applicability of the findings in the field of innovation management. Conclusions: implementation of the proposed forecasting algorithm, based on step-by-step logic and taking into account the principles of systematicity, scientific verifiability, and scenario variability, can improve the accuracy and managerial value of forecasts. This methodology allows for the substantiation of strategic decisions aimed at developing regional innovation systems. In the context of digitalization, the integration of big data and digital trace analysis into the algorithm is crucial, enabling the identification of hidden patterns and enhancing the proactiveness of management responses. At the same time, the sanction factor, designed to model crisis contours and develop preventive measures to ensure the technological resilience of the regional innovation system, is becoming increasingly relevant.

regional innovation system, innovation system development, forecasting, forecasting principles, forecasting stages, neural network technologies, innovation development assessment, development scenarios