Abstract

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.

Keywords

strategizing, strategic goals, sources of financing, artificial intelligence, multi-agent modeling, integrated financing route, personal investment route, data economy

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