Maksim Slavyantsev

Artificial intelligence and machine learning methods build investment routes to balance models between private and public sources of financing. In this respect, they are of national importance for import substitution and technological sovereignty. Decision support systems build business development scenarios based on marked-up data. They reduce the risks of projects connected with import substitution and national technological sovereignty. Early integrated planning and balancing of developer and investor capabilities can help other venture and high-tech projects by balancing various sources of private and government financing. This article introduces a new development method of machine learning and artificial intelligence based on an ultraprecise neural network. The method automates the task of navigating technological projects using investment financing tools. It builds a continuous multi-agent investment route to reduce the risks of technological projects in terms of private and government investments. In fact, the method offers an algorithm that connects the fundraising stage, the type of project, and the type of funding source. The research objective was to strategize the development, implementation, and scaling of artificial intelligence methods and scenario multi-agent modeling to solve economic coordination tasks of raising public and private funds by personal investment routes and integrated investment routes. The authors rationalized the development, implementation, and scaling of personal and integrated investment routes, defined the development principles, and designed a checklist. They also developed a methodology for using artificial intelligence algorithms. The practical part featured a case of strategizing regional economic potentials in terms of raising additional funds by multi-agent modeling of financial and economic interaction of individual investment projects and integrated investment projects. The authors assessed the long-term multiplicative effect of investment projects on sectoral and intersectoral cooperation, which increases the regional investment attractiveness. The study relied on the theory of strategy and methodology of strategizing developed by Professor Vladimir L. Kvint.

In 2024, Russia chairs the BRICS, having defined its global strategy as “strengthening multilateralism for equitable global development and security”. According to the methodology developed by Prof. V.L. Kvint, the global strategy depends on the relevant international, national, and corporate strategies of companies from the BRICS countries. Data economy uses algorithms for data processing and analysis, including AI solutions. Prof. V.L. Kvint defines the strategy as a combination of wisdom, a correct attack vector, and an adequate assessment of resources. Prof. A.A. Romanov and Prof. V.V. Kondratiev used Model-Based Systems Engineering 2.0 to adapt Prof. L.A. Bakhvalov’s methodology of system modeling to the modern conditions. All these principles can yield a new approach to multi-criteria modeling of related cross-country and cross-industry corporate strategies, especially if facilitated by the digital twin method and the interoperability policy. The major construction industry is high on the BRICS agenda in terms of large infrastructure and industrial facilities. Therefore, Model-Based Systems Engineering 2.0 provides good prospects for development strategy modeling.

The current global situation involves turbulent financial markets, new geopolitical reality, advanced digital technologies, and multimodal transport corridors. Therefore, to strategize the development of an economic branch and its interaction with foreign markets, one should proceed from new principles of technological connectivity of industries, the importance of resources in the global value chain, the qualityandprice balance, reliable market access opportunities, transport accessibility, cybersecurity, etc. Typification of a natural resource has become especially important in the new conditions. If it is renewable, the long-term goal of its capitalization is to preserve or multiply. If it is non-renewable, the main goal in the formation of national welfare is the maximal output and recultivation. This typification of resources presupposes that a raw material economy has sufficient technological, technical, financial, and political potential to process the resource at the place of its extraction and receive sufficient revenue to either preserve and multiply this resource or to provide the maximal output and reclamation. Access to labor resources and demand market determines the main goal of an economy that depends on the import of natural resources. It involves generating revenue from the production and sale of the final products and services in a place of solvent demand and the additional need to create national welfare based on the existing political, intellectual, and technological resources. Technological sovereignty requires new cooperative cross-country strategies for the interaction of industries and businesses. Technological sovereignty needs a scenario-predictive modeling of the potential of coordination mechanisms to develop mutually beneficial interaction between industries during the high turbulence on foreign markets. The article introduces methodological tools for the development of cooperative cross-country strategies between industries and business. It includes a novel interpretation of the adaptation vectors for national strategies and corporate interests in food security, based on cooperative intersectoral and cross-country development strategies. After the Second Russia-Africa Summit, the article might be useful for the authorities responsible for the technological sovereignty, development, and leadership of Russia.