The decision tree neural network as part of a cognitive model for forecasting the sustainability of the Russian economy

This study addresses the problem of sustainable economic growth, a subject that is highly relevant in the current conditions of market uncertainty. Given the importance of having an accurate forecast of GDP in uncertain market conditions, this study proposes a digital cognitive model that includes an artificial intelligence (AI) system decision tree for forecasting GDP values. This study aims to test whether using a cognitive model with the application of the AI system decision tree can afford a more accurate forecast of GDP than known forecasting methods. To achieve this goal, this study: 1) investigated the theoretical fundamentals of sustainable economic growth; 2) identified the development trends of AI systems in economics and finance to create the model’s dataset; and 3) calculated the forecast value of GDP using the digital cognitive model that included the AI system decision tree. The methodology involves the formation and use of a cognitive model that uses a decision tree neural network based on the Python language in the Google Collab cloud environment. Further, monographic, analytical, and computational-constructive methods were used. The results showed that the developed digital cognitive model, which included an AI system decision tree, was capable of forming GDP forecast values under changing external parameters.