INNOVATION PROCESSES IN COMPLEX SYSTEMS: ISSUES OF SYSTEMS ANALYSIS AND MODELING

Abstract

This article analyzes the essence of innovation processes, their structural elements, interconnections, and self-organization mechanisms from the perspective of complex systems theory. The processes of generation, diffusion, and adoption of innovative ideas within a systemic approach are examined. A comparative analysis of modern methods for modeling and forecasting complex systems - agent-based modeling, system dynamics, network analysis, nonlinear dynamical systems, and machine learning approaches - is presented. The findings are of practical significance for managing complex information systems and enhancing innovation potential in the digital economy.