Fundamentals of the system-synergetic theory of information and the universal informational variational principle of system development

The article outlines the conceptual foundations of a unified system-synergetic theory of information, which represents a significant step forward compared to classical approaches. This theory is the result of a formal synthesis of two leading Russian scientific schools: the synergetic theory of information, which describes information through the prism of dynamic self-organization processes, and the systems theory of information, which defines information through the structural-hierarchical and emergent properties of systems. Unlike classical theory, which views information as a measure of diversity within a set of unrelated elements, the proposed approach introduces the concept of a «system», where interconnections between elements play a pivotal role. This allows for the quantitative measurement of such previously purely qualitative concepts as complexity and emergence. The central result of the theory is the formulation of the universal information variational principle. This principle postulates that the development of any open system — from physical and biological to economic and social — occurs in such a way as to maximize the rate of information increment. It is proposed that this principle be viewed as one of the key regularities of evolution, determining the direction of system development toward increasing complexity and order. The theory offers specific metrics for measuring systemicity and complexity, such as the coefficient of emergence, which indicates how many times the information capacity of a system exceeds the information capacity of a simple set of its elements. In conclusion, this work offers an approach to overcoming a number of limitations inherent in classical information theory and formulates a fruitful program for future research. It opens new horizons for understanding and modeling complex systems by offering a unified explanatory mechanism for a wide range of phenomena. Practical applications of the theory include the development of new methods for big data analysis, the creation of more adaptive and self-learning artificial intelligence systems, and the forecasting of market and social network development. Thus, the proposed theory lays the foundations for the creation of a unified science of complexity, uniting the efforts of scientists from various fields.