Mathematical modeling of digital ecosystems based on distributed registries and neural network algorithms

Abstract

This article presents mathematical modeling of digital ecosystems based on distributed registries and neural network algorithms. The relevance of the research is due to the rapid development of digital technologies, the growth of data volumes and the need to increase the security and transparency of information flows. The introduction substantiates the importance of studying the synergy of distributed registries (blockchain technologies) and artificial intelligence methods to create reliable, scalable and adaptive digital ecosystems capable of meeting the needs of the modern economy and ensuring resilience to external threats. The methods section presents a mathematical model that combines the approaches of graph theory, stochastic modeling, and algorithmic data processing using neural network structures. The methods of calculating and analyzing the distribution of transactions, modeling node interactions, and evaluating the probabilistic characteristics of the system are described. The Monte Carlo method was used to validate the model, which makes it possible to assess the stability and dynamic variability of the digital ecosystem under various load scenarios and possible failures. The results of the study demonstrate that the integration of distributed registries with neural network algorithms contributes to a significant increase in data processing efficiency and system security. Experimental data confirm that the proposed model makes it possible to optimize the speed of transactional processing, reduce the likelihood of errors and minimize the risks of unauthorized access. In addition, the model revealed the relationship between the parameters of neural network optimization and the degree of decentralization of digital ecosystems, which opens up new prospects for scaling and adapting technologies. The discussion section analyzes the results achieved, compares them with traditional modeling methods, and highlights the advantages of the proposed interdisciplinary approach. The potential for further research aimed at improving algorithmic structures, integrating additional layers of security, and adapting models to the specific requirements of various industries is noted. Thus, the article makes an important contribution to the development of the theory and practice of digitalization, stimulating further developments in the field of mathematical modeling and optimization of digital ecosystems.