π-Economy

RAR RUS 8-21 V-1094-2019 56968223000 0000-0002-0941-6358 Peter the Great St.Petersburg Polytechnic University Babkin Alexander babkin@spbstu.ru

Russia, 195251, St.Petersburg, Polytechnicheskaya, 29

Liberman Irina iliberman@kantiana.ru Klachek Pavel PKlachek@mail.ru Industry 5.0 and intelligent economy: fundamentals of neuro-digital transformation of cyber social meta-ecosystems of high-tech industrial complexes Currently, the concept of neuro-digital transformation is being actively introduced into the theoretical and practical field of activity of the digital economy and Industry 5.0. In future studies, on the basis of active implementation and development, methods and tools for neuro-digital transformation of industry developed to date, including in macro-level companies (SpaceX, Carbon Valley (Rostec), etc.), the authors plan to start creating a multivariate design for the scientific and technological development of high-tech industrial complexes based on methods and tools for neuro-digital transformation and intelligent economy. The development of a system-targeted scheme of the cognitive framework of cyber social ecosystems, based on the model of neuro-digital intelligence and the system-synergistic concept of neuro-digital transformation of cyber social ecosystems, allowed the authors to develop a system-targeted scheme of a system tetrad of cyber social meta-ecosystems for the development of high-tech industrial complexes. Based on this scheme, the authors developed and implemented a complex of applied cognitive production management systems for the development of high-tech industrial complexes. Development of conceptual and methodological foundations of neuro-digital transformation and cyber social meta-ecosystems of Industry 5.0. high-tech industrial complexes allows you to move on to the creation and testing of applied tools for managing the development of cyber-social meta-ecosystems for the design of high-tech industrial complexes in the conditions of Industry 5.0. and intelligent economics. The purpose of the study is to develop a system-synergistic approach and a tool (platform) for the neuro-digital transformation of high-tech industrial complexes. Main results presented in the article. Based on the system tetrad and the system-synergistic concept of neuro-digital transformation of cyber social ecosystems, a system-targeted scheme for creating a system-synergistic tetrad of cyber social meta-ecosystems for the development of high-tech industrial complexes was developed. The authors disclosed a method of simulating neuro-digital transformation of cyber social meta-ecosystems for the development of high-tech industrial complexes based on a multi-agent genetic algorithm. A block diagram and application software of a multi-agent genetic algorithm was developed. The architecture and software of the application tools for managing the development of cyber-social meta-ecosystems for the design of high-tech industrial complexes in the conditions of Industry 5.0 and the intelligent economy were developed. The application tools for managing the development of cyber social meta-ecosystems for the design of high-tech industrial complexes were tested, in the conditions of Industry 5.0 and the intelligent economy, to solve the problem of modeling the neuro-digital transformation of cyber social meta-ecosystems of high-tech agro-industrial complexes of the Kaliningrad region. 10.18721/JE.16501 621:319.23 intelligent economy Industry 5.0 cyber social ecosystems artificial intelligence high-tech industrial complexes https://economy.spbstu.ru/article/2023.103.1/ 01_Babkin%2C-Liberman%2C-Klachek.pdf

RAR RUS 22-37 Nikonova Allla prettyal@cemi.rssi.ru Russia's Technological Sovereignty: Research and Modeling from the Standpoint of System Transformation of the Economy Relevance. The problem of technological sovereignty is being explored in the context of sanctions against Russia. The goal is to form an approach to balanced solutions in ensuring technological sovereignty in end-to-end technologies through domestic R&D and intellectual potential. Methodology and methods. The provisions of the system economic paradigm are used as the basis of the system analysis and synthesis of the Russian economy, modeling the interactions of key sectors of the social system, and formulating conclusions. Results. A significant degree of dependence both on foreign technologies and on other components of imports is presented in dynamics and in the context of activities. The possibilities and limitations of Russia's technological sovereignty in relation to the field of information technology as one of the critical ones are given. Two scenarios of technological sovereignty are considered: import substitution and a radical transformation of the economic model as a whole. For this, simulation models are presented. Novelty. Structural and functional models based on platform solutions in the field of IT imitate the interactions of enterprises searching for technologies within a system that includes not three, but four collective actors. Unlike traditional approaches, the division of economy on the business and enterprise sector, which differ in goals (profit and continuity of the reproduction cycle), allows you to avoid the dominance of any one goal in ensuring technological sovereignty, to overcome the contradiction between short-term and long-term goals. The results of a systematic approach lead to network structures without loss of functionality in conditions of limited resources, provide scientific knowledge about the importance of collaboration between actors as a fundamental factor in the transformation of the economic model and technological sovereignty in the long term. Conclusions. Collaborative forms of interaction serve as a means of self-organization and formation of innovative ecosystems. A systematic view of the structure and functions of actors contributes to the development of balanced economic, institutional, organizational measures to ensure technological sovereignty and increase their validity. The tetrad representation of the functions of the four actors allows us to explore ways to obtain end-to-end technologies not only in the field of IT; this also applies to further research on the topic of Russia's technological sovereignty. 10.18721/JE.16502 338.24 Russian economy system import information technology enterprises (companies) business state science and education interactions https://economy.spbstu.ru/article/2023.103.2/ 02_Nikonova.pdf

RAR RUS 38-50 Orlova Ekaterina ekorl@mail.ru Reinforcement Learning as an Artificial Intelligence Technology to Solve Socio-Economic Problems: Algorithms Performance Assessment Reinforcement learning is a class of machine learning and artificial intelligence methods, a field for the applied problem studied, as well as methods for solving it. One of these problems is management in social and economic systems, designing optimal control taking into account the systems’ properties such as variety of characteristics scales, heterogeneity of data samples, incompleteness and gaps in the data, data stochasticity, their multicollinearity and heteroscedasticity. Reinforcement learning methods are not sensitive to these features and can be used with higher efficiency in various applications of economics, finance and business. Reinforcement learning is closest to the way humans learn, and solutions to emerging problems can be found in the field of biological self-learning systems based on the principle of trial and error. Reinforcement learning methods are a computational approach to learning, when the control subject (agent) learns under interaction with a complex, dynamic, often stochastic, control object (environment) like a socio-economic system in order to maximize the total reward. In the process of modeling, the problem of choosing such learning algorithms that adequately reflect the stochastic dynamics of the modeled object and have high performance is very important. Business and quality metrics that are appropriate for assessing the quality of supervised and unsupervised learning methods in machine learning are not entirely suitable for evaluating the effectiveness of reinforcement learning methods, since there is no empirical data for evaluation. The paper proposes a number of quality indicators of training for managerial decisions generated on the basis of training methods with reinforcement learning. We use an example for the corporate human resources management. A comparison for learning algorithms such as DQN, DDQN, SARSA, PRO for designing optimal trajectories for the proficiency training of the personnel is made. An assessment of the proposed quality indicators for the entire group of learning methods is carried out and one of the algorithms with the highest performance is selected. 10.18721/JE.16503 519.857.3 socio-economic systems individual trajectories for employees’ development artificial intelligence machine learning reinforcement learning quality of learning algorithms https://economy.spbstu.ru/article/2023.103.3/ 03_Orlova.pdf

RAR RUS 51-62 Silkina Galina galina.silkina@gmail.com Shaban Anton Digital innovation: essential characteristics and features Digitalization has acquired a total socio-economic nature, which means that digital innovations have had a large-scale effect in the economy: they play the role of basic, industry-forming, and developing innovations as the pace of product restock and business process improvement increases. The actual situation requires understanding the essence of digital innovations, their technological features and resource capabilities in achieving economic progress. The subject area of the study is the field of management in its general economic and applied context. The latter is considered from the standpoint of substantiating and making managerial decisions to ensure digital transformation. The information and analytical review carried out by the authors allows us to state that the theory of digital innovation is in the process of accumulating knowledge and developing scientific schools. In the works of foreign and Russian scientists, there is a clear convergence of ideas of digitalization and innovative development of the economy, the effect of which is substantiated by an assessment of the potential of open innovations and network forms of organizing innovative activities, creating innovative ecosystems that operate using information and communication technologies in their digital format. A digression into economic practice proves an equal interest in scientific substantiations, developments and solutions in the real and financial sectors of the economy, with appropriate institutional support from the state regulation system. The object of this study is digital innovation; the subject of the study is the essential characteristics of digital innovation, determining approaches and methods of their management. The essence of digital innovation, that is the innovation ensured by the use of digital information technologies, is revealed by the authors from the perspective of their refined concept of the digital economy as an economic structure, which presupposes the widespread formation of digital competencies at the level of economic systems and their implementation in justifying management decisions. The characteristic features of the digital economy are: data-centric approach to decision making; dynamic competitive landscape, customer-centricity, platform business models with an ecosystem perspective. In substantiating the essential characteristics of digital innovation, the authors proceed from an understanding of the rational nature of the modern organization of innovation activity. Within the framework of the article, the terminological apparatus was clarified by introducing the author’s interpretation of the categories “digital economy” and “digital innovation”; the relationship between them is identified and justified; the basic characteristics of digital innovations and their specific features are determined. 10.18721/JE.16504 65.012 Digital economy digital innovations open innovations network model of organization of innovation activities digital innovation projects flexible management methodologies https://economy.spbstu.ru/article/2023.103.4/ 04_Silkina%2C-Shaban.pdf

RAR RUS 63-77 Q-4229-2017 57195759467 0000-0003-3644-4239 Voronezh State Technical University Shkarupeta Elena 9056591561@mail.ru

20 letiya Oktyabrya st., 84, Voronezh, Russia

Dolganova Iana Yanochkadol@yandex.ru Peryshkin Mikhail Intelligent digital technopolis in the context of improving economic security of depressed regions The relevance of the study of intelligent digital technopolises in the context of economic security of depressed regions is determined by the need to integrate advanced technologies and innovative management methods to stimulate regional development. The aim of the study is to develop methodological and practical recommendations for the creation and functioning of such technopolises, taking into account specific technological and socio-economic factors of depressed regions. The study applied the methods of systematic literature review and web scraping, which allowed us to conduct a deep analysis of existing models of technopolises and their applicability in depressed regions. As a result of the study, the concepts of intellectual economy were analyzed in terms of differentiating approaches to intelligent and intellectual economy. It is concluded that technopolis in depressed regions not only stimulates economic activity, but also forms a stable basis for long-term development, which is a key factor of economic security in the conditions of intellectual and digital economy. A comprehensive analysis of the existing models of technopolises with a focus on their applicability in depressed regions has been carried out. This aspect is innovative, as it has not been previously considered in the context of the specifics of the intellectual digital technopolis model. The key technological and socio-economic factors influencing the successful functioning of intelligent digital technopolises were identified. Within the framework of the research, the classical technopolis wheel was modified for the first time, which allows taking into account the factors of intellectualization and digitalization. Methodological and practical recommendations for optimizing policy in the field of creating and supporting the functioning of intelligent digital technopolises in depressed regions are formulated. The recommendations take into account the unique characteristics of depressed regions. The results of the study can serve as a basis for research and practical application in strategic planning and development management of depressed regions. Further research can be aimed at studying the impact of macroeconomic factors on the success of smart digital technopolis projects in depressed regions, as well as the development of indicators and metrics to assess the effectiveness of smart digital technopolises from the point of view of economic security. 10.18721/JE.16505 338.1 Intelligent economy digital economy cluster technopolis depressed region economic security https://economy.spbstu.ru/article/2023.103.5/ 05_Shkarupeta%2C-Dolganova%2C-Pyorishkin.pdf

RAR RUS 78-90 Polyanina Polina Rodionov D.G. rodion_dm@mail.ru Konnikov Evgenii konnikov.evgeniy@gmail.com Modeling financial market conditions in an intelligent economy based on a fuzzy set approach In the era of globalization, there is a high degree of interconnection between a country's economy and the state of its financial sector. Effective functioning and dynamic development of the financial sector become an urgent need for ensuring stable economic growth. However, quite often, many developing countries on their path to this development face a series of constraints. These restrictions can seriously affect their financial potential, hindering the development of financial systems. Given these factors, the importance of overcoming them and searching and developing the latest innovative methods for analyzing financial phenomena and processes comes to the fore and become a pressing task of the present. Following this trend, this paper presents the author's model of estimating the state of the financial market. The comparative basis for this assessment was the integral indicator of the state, formed based on partial estimates of financial depth, access to finance, financial stability, and financial efficiency. The foundation for it was the methodology of fuzzy-set modeling, the purpose of which, regarding the issues under investigation, is in-depth study of the influence of financial structures on economic growth and the classification of financial indicators. Applying this model in practice, the authors have collected and analyzed extensive arrays of data concerning integral indicators of access to finance, financial depth, stability, and efficiency for two countries, Russia and the USA, and conducted a comparative analysis of the financial markets' changes during the selected period. The obtained results and observations allow to conclude that, unlike the USA, where instability and negative dynamics are observed, the financial market of Russia remains relatively stable during the period under review. Thus, on the basis of applying this model, it is possible to develop a more effective financial and banking policy. The model provides significant opportunities for deep and comprehensive analysis of financial phenomena and processes, which contributes to a more accurate assessment of the state of the financial market and rational forecasting of its future development. 10.18721/JE.16506 330.47 fuzzy multiple approach fuzzy set integral indicator financial depth financial stability financial ability financial efficiency state of the financial market https://economy.spbstu.ru/article/2023.103.6/ 06_Polyanina%2C-Rodionov%2CKonnikov.pdf

RAR RUS 78-90 V-1094-2019 56968223000 0000-0002-0941-6358 Peter the Great St.Petersburg Polytechnic University Babkin Alexander babkin@spbstu.ru

Russia, 195251, St.Petersburg, Polytechnicheskaya, 29

Q-4229-2017 57195759467 0000-0003-3644-4239 Voronezh State Technical University Shkarupeta Elena 9056591561@mail.ru

20 letiya Oktyabrya st., 84, Voronezh, Russia

Tashenova Larisa Methodology for assessing the convergence of digital industrialization and industrial digitalization in the conditions of Industry 4.0 and 5.0 The relevance of the study of the convergence of digital industrialization and industrial digitalization is due to the significant impact of digital technologies on modern economic systems, where they act as a catalyst for innovation, modernization of the industrial structure and sustainable development within the framework of the concepts of Industry 4.0 and 5.0. The purpose of the study is to develop an author's methodology for assessing the convergence of the digital industrialization and industrial digitalization in the conditions of Industry 4.0 and 5.0. This will fill the existing gap in the scientific literature and provide a tool for analyzing and comparing the effectiveness of digital applications in various economic systems. Particular attention is paid to the adaptation of this methodology for the conditions of Industry 4.0 and 5.0, which enriched the theoretical basis and practical recommendations in this area. The study applied methods of descriptive, criteria, quantitative, comparative and trend analysis; methods of systematization, aggregation and normalization. The source of initial data in dynamics from 2010 to 2022 was Rosstat in terms of information society indicators as of September 2023. The results of the study consist in the development and successful testing of the author's methodology for assessing the convergence of the digital industrialization and industrial digitalization in general in the Russian Federation, as well as in a comparative analysis of the dynamics of this process between Russia and China. The method includes 6 main stages. The novelty of the work lies in creation of a unique tool for convergence analysis in Industry 4.0 and 5.0, which includes a system of indicators for assessing the convergence of digital industrialization and industrial digitalization from 2 primary, 7 secondary and 19 tertiary indicators, which fills the existing gap in the academic literature. The practical value of the study is expressed in recommendations formulated on the basis of empirical data to stimulate the sustainable development of the digital economy in Russia. Further research may focus on detailing the convergence assessment methodology across industries and geographies, integration with other indicators of sustainability and resiliency, and effectiveness analysis for different stages and mechanisms of digital adoption. 10.18721/JE.16507 330.341 Digital Economy Industry 4.0 Convergence Digital Enterprises Industrialization Digitalization https://economy.spbstu.ru/article/2023.103.7/ 07_Babkin%2C-Shkarupeta%2C-Tashenova.pdf

RAR RUS 109-122 56502340400 0000-0002-1685-2625 Russian State University for the Humanities Vertakova Yulia vertakova@rambler.ru

Miusskaya sq. 6, Moscow, 125047, Russia

Bulgakova Irina mmio@amm.vsu.ru Din Shui Methods and tools of digital transformation of agroindustrial enterprises in the context of Industry 4.0 Relevance. In the context of Industry 4.0, agricultural enterprises are trying to find ways to increase their efficiency and competitiveness. They must be ready to use new technologies and adopt new production management methods in order to increase efficiency, flexibility and quality of production. One of the ways to achieve this goal is to follow global trends, one of which is the digitalization of management activities. Digitalization of such an area, which is not often mentioned in modern literature, as the agro-industrial complex, is a priority and requires additional research and development. The purpose of the study is to consider methods and tools of digital transformation of agro-industrial enterprises and to offer tools for choosing the most suitable digital platform according to a set of selected criteria that take into account the specific requirements of agro-industrial enterprises. In the course of the research, general scientific methods of analysis and synthesis were used, as well as special methods of economic and statistical research (the method of discrete optimization). The article discusses the main changes that occur under the influence of digital transformations of Industry 4.0, methods and tools of digital transformation of enterprises in various fields of activity, criteria for choosing digitalization tools, types of digital platforms. Applied research was conducted for agricultural enterprises. The most famous digital platforms are considered, it is shown how to integrate "end-to-end" technologies into them. It is shown how to form criteria for choosing digitalization tools for making digital decisions in the management of enterprises of the agro-industrial complex. The method of discrete optimization is tested, the combinatorial set cover problem is solved. The analysis made it possible to summarize what requirements consumers have for the functionality of digital platforms and to prove that combinations of FarmLogs and Climate FieldView platforms or FarmLogs and John Deere Operations Center can fully meet the needs of an agro-industrial complex enterprise at the present stage of its digital transformation. Conclusions: It is proved that for the agro-industrial complex, the choice of a digital platform and the "end-to-end" technologies used in them can be carried out by the method of discrete optimization by solving the set cover problem. Directions for further research: development of the proposed approach, its adaptation to the specific conditions of various subsystems of the agro-industrial complex. 10.18721/JE.16508 332.024 digital transformation digital platform agro-industrial complex discrete optimization https://economy.spbstu.ru/article/2023.103.8/ 08_Vertakova%2C-Bulgakova%2C-Din.pdf