In the context of exponential growth of technological capabilities and increasing data volumes, artificial intelligence (AI) can not only improve the accuracy of forecasting, but also neutralize the influence of cognitive biases, which significantly simplifies the decision-making process. AI separates forecasting from judgment, complementing a person in the decisionmaking process, most accurately calculating probabilities, thereby repeating the work of human System-1 which also automatically calculates probabilities, but due to the heuristics of judgments makes predictable systematic errors. The article reveals the advantages of using AI algorithms: first, a decrease in cognitive load when considering alternatives, second, a more accurate forecast that is not amenable to framing, third, the ability to build a choice architecture within the framework of a nudge policy. It is argued that automation and AI algorithms are beginning to influence individual decisions, raising questions about the nature of autonomous choice and rationality in the digital age. In practice, the symbiosis of humans and AI, widely used in marketing, retail, etc., allows us to build simulation models of realistic multi-agent behavior, conduct dynamic segmentation and create personalized content based on a large array of consumer preference data, adapt innovative products and customize individual offers. The study uses an interdisciplinary methodological approach based on a synthesis of approaches from behavioral economics, marketing and AI sciences. The authors conclude that the dynamic partnership of AI and humans has a powerful transformative potential in various areas of life, from optimizing business processes to improving the quality of our lives.

Under inflation targeting, the success of monetary policy largely depends on the central bank’s ability to influence inflation expectations through effective communication. Particular importance is attached to interaction with non-specialists, the general public, whose expectations in Russia have historically been elevated and less anchored. This article examines the impact of readability of Bank of Russia press releases on public inflation expectations. Readability is a property of textual material that reflects how easily it can be perceived during reading. To test the hypothesis, the authors conducted a laboratory experiment with 274 participants divided into control and experimental groups. The participants received either original or simplified versions of press releases, with more readable versions generated using generative artificial intelligence. The experiment simulated both easing and tightening monetary policy conditions. The results showed that in the case of an interest rate hike, simplified texts significantly reduced inflation expectations—on average by 1.8–2 percentage points. In the case of a rate cut, the effect was statistically insignificant. Thus, improving the clarity of communication is especially effective in a tight monetary policy context. The findings highlight the need to adapt central bank communication materials for a broad audience. The study concludes that enhancing readability can increase public trust in monetary authorities and strengthen the transmission mechanism of monetary policy. The work relies on methods of experimental economics, textual analysis, and regression modeling, confirming the importance of communication quality in managing inflation expectations.

Periods of high market volatility put investors in a situation where conventional decisionmaking methods are less reliable. To improve returns, market participants need to understand which factors play a big role in portfolio formation. This article analyzes the determinants of Russian stock returns during the Covid-19 and the rise in geopolitical tension in 2022. The objective of the study is to identify common determinants of returns on individual stocks in an environment of increased volatility in order to formulate recommendations for investors on capital allocation. The study is carried out in the last two periods of increased volatility in the Russian stock market using quote data and fundamental financial indicators of company valuation. It was found that return on assets (ROA) and stock return for the previous calendar year have a significant positive impact on securities returns during both periods of market uncertainty. Based on these indicators, the Data Envelopment Analysis (DEA) method was used to form an assessment of company's performance and build stock portfolios. Portfolios of the best DEA-ranking companies significantly outperformed portfolios of the worst and the benchmark. The portfolio of the best stocks outperformed the portfolio of the worst DEA-ranking stocks by 12.50% during the Covid-19 pandemic and by 31.45% during the 2022 geopolitical. The findings have high practical value for investors, as they allow capital to be allocated to more promising stocks during the periods of market stress.

The problem in development of manufacturing and consumer industries largely stems from the specifics of tax burden, but there are no verified calculations of the industry-specific burden. An important task is the creation of tax burden formulas, calculations of industries’ differences in the burden, and interpretation of the results by key aspects of tax legislation. The Federal Tax Service publishes serious statistics and analytics on tax receipts by industry, details of tax base for each tax (better than Rosstat). Annual materials for the federal budget contain details of assessing tax benefits (tax expenses) by industry and type of tax. These are the basis of the study, processed by the methods of mathematical formulas and logical analysis. The findings show that manufacturing industry is actually subject to 1.5 times higher taxes than consumer sectors. Wholesale trade, hotels and catering, finance and real estate pay VAT at an effective rate of 1-4%, producers of coal, ore, and petroleum pay negative VAT, and producers of electronics, equipment and vehicles pay an average of 15%. The actual rates of social security contributions in manufacturing is 23%, and in trade, finance, hotels and catering, and real estate from 6 to 13%. The main conclusions show the specifics of VAT refunds on exports and capital investments for mining, the concentration of small businesses and the simplified tax system in consumer sectors. We need to redistribute the burden of VAT and social security contributions by 6 and 5 percentage points plus for consumption industries and minus for manufacturing, at least to equalize the actual tax burden.

The article studies the features of high-tech industries as a locomotive of national economy, vertical production integration, joint international projects and their advantages in the current political situation and also total profits distribution of the companies implementing a joint project. The objective of the paper is to work out the methods to increase the profitability of joint international projects and construction of the set of economic and mathematical models for calculating transfer prices, representing the mechanism for the total profits of a joint international project to be distributed between its participants. The author proposes to emphasize the following methodological approaches: the basic assumptions of firm theory, international financial reporting standards (IFRS), linear optimization methods, iterative algorithms for nonlinear equations and their convergence based on fixed point theorem, labor unit costs model assessment, with learning curve effect being applied. The results of the work contribute to managing the profitability of a joint international project based on the developed model complex for the formation of transfer prices and also show the leading role of aerospace industry and nuclear energy in the development of national economy, and can be applied in forming business plans of joint international projects. It is concluded that attracting foreign investments in high-tech, science-intensive industries of the Russian economy in the format of joint international projects for the production of new-generation products provides access to modern technologies, increases the workload of related domestic industries.

This work identifies the factors associated with the value of box office of films released in Russian distribution in the period from 2014 to 2018. The authors use regression analysis and the method of principal components and show that a 1% increase in the number of audience reviews increases the box office of a movie at the Russian box office by 0.4%, while a 1% increase in the potential audience reach of a movie trailer increases the box office by 0.3%. The box office value is related to the content of movie critics' reviews. The number of reviews that are subtle praise (‘extraordinary’, ‘magnificent’, ‘masterpiece’, ‘remarkable’, etc.) is on average negatively related to box office, while the number of reviews that include words characterizing tense atmosphere (‘haunted’, ‘scares’, ‘creepy’, ‘monster’, etc.) or scenes involving violence and danger to life (‘gun’, ‘violent’, ‘brutal’, ‘terrifying’) are positively related. For biographical films, critics' use of words from the ‘praise’ vocabulary is positively related to box office, while words from the ‘suspense’ vocabulary are negatively related to box office. The number of reviews containing critics' description of violent scenes is negatively related to the box office of thriller or action films. The number of reviews containing words from thematic dictionaries (‘feelings and emotions’, ‘money’, ‘kingdom’, ‘military themes’, ‘romance’) are related to the box office receipts of films, with the nature of the connection depending on the genre. Thus, critics' use of words from the vocabulary ‘money’ is positively related to the box office value on average, but negatively for animated films.

A sharp rise in the cost of passenger air transportation services in Russia in 2024 led to an increased discussion of flight accessibility for Russian consumers. According to industry experts, the trend of significant growth in air travel prices will continue in the coming years. The key research objective is to determine the dynamics of the affordability of passenger air transportation in Russia, identify the key factors (objective economic, market, consumer) and the nature of their impact on the cost of this service. The study is based on a statistical and econometric analysis of data from the FSGS and the largest airlines on the average cost of flights in 2004-2022 and ticket prices in 2023. Considerable attention is paid to reviewing the results of earlier studies of the problem. The authors argue that, despite a steady annual growth of air transportation in the last decade, the dynamics of Russian airlines' production performance since 2020 has been determined by various exogenous shocks (the pandemic crisis and aggravation of geopolitical tensions). The time-delayed nature of the impact of macroeconomic and geopolitical factors on the cost of flights has been established, smoothed out by measures of government regulation and support for the aviation industry. It is proved that, despite the trend of annual growth in nominal value, passenger air transportation services are becoming more accessible to the Russian consumer in real terms. The work concludes that objective economic conditions can have an impact on the cost of passenger air transportation services in the long term, however, the current short-term significant volatility in airline ticket prices is mainly determined by market conditions and consumer choice. In the near future, the price dynamics in the passenger air transportation sector will be influenced primarily by the pace of development of tourist destinations within the country and the growth of company costs, as well as government regulation and support measures for the industry.

The development of Russia's digital economy faces a contradiction between the need for free big data exchange and increasing control over personal data. The introduction of «Data Economy» national project and new federal laws on personal and depersonalized data has intensified regulatory problems in this sphere. The research aims to identify economic problems in big data field arising from regulatory gaps and new personal information protection norms. The methodological foundation is new institutional theory, particularly O. Williamson's transaction cost economics. The author applies comparative institutional analysis and economic-mathematical modeling methods to assess the effectiveness of penalty sanctions. Differences in the specificity of big data and personal data as resources were established, justifying the need for a differentiated regulatory approach. Structural regulatory alternatives were identified: from complete state control to market mechanisms with intermediate hybrid forms. The main obstacles to big data market development are uncertainty regarding anonymized data status and the absence of reliable depersonalization methods. Modeling showed that introducing turnover-based fines creates excessive burden on small and medium-sized enterprises that previously invested in cybersecurity. Ensuring digital industry development requires mandatory public-private partnership in rulemaking through self-regulatory organizations that account for a high pace of technological changes.

The small and medium-sized enterprise (SME) sector is highly dynamic: approximately one million new firms are established each year, yet not all manage to survive. This study examines several intrinsic demographic processes within the SME sector. Specifically, it aims to assess the age structure of SMEs and provide a quantitative evaluation of their survival rates. The analysis is based on microdata from SME registries within the span from 2017 to 2023. Each annual dataset comprises over six million observations. To enable macro-level insights from microdata, the authors developed a methodology for collecting and analyzing registry data. The analysis places particular emphasis on the age at which firms cease operations. The findings offer valuable insights into the scale and patterns of natural selection processes affecting newly established SMEs. The resulting estimates may inform prospective entrepreneurs, academic researchers, and policymakers involved in designing support mechanisms for small businesses and fostering a conducive business environment. Key findings include the following: approximately one-third of newly created firms exit the market within the first two years of operation: less than 30% of new enterprises survive to reach five to seven years of age; most new firms begin as microenterprises, which remain the dominant category across all age groups. Among firms of five to seven years, the combined share of small and medium-sized enterprises is less than 4%; the overall age structure of the SME sector is skewed toward younger firms, with this skew particularly pronounced among individual proprietors; the sectoral composition of new firms remains relatively stable over time, with trade, transportation, and construction consistently ranking as the top three industries.

The spread of life-long education in the world and its growing significance among Russian citizens is confirmed by the state policy of subsidizing sale and consumption of educational products in the form of advanced training and professional retraining programs. The development of new tools to manage this process is relevant for economic development of the country regions in terms of labor capital growth in the labor market. The methodological basis of the work consists of a systematic research method, analysis of design solutions, analysis of scientific sources and publications of government bodies official data, information resources on strategic development plans of the country («National Priorities»), major players in the real economic sector, and a methodology for quantitative analysis of the dynamics of the number of students in additional education programs, matrix methods for researching trends in the development of e-commerce, statistical methods for researching information and analytical materials of the Federal State Statistics Service, as well as data from the considered segment of the regional educational market (drawing on the implementation of educational product in vocational training and additional education for certain categories of citizens in the Vladimir region). The authors define the role of additional vocational education in the context of national economic development of the state, including e-commerce, and analyze the importance of e-commerce as a factor in increasing regional labor potential, the competitiveness of citizens in regional and sectoral labor market. The mechanism of managing socio-economic processes of the sphere of educational activity carried out by regional employment services are analyzed, a map of educational products implemented as part of the national project (on the example of the Vladimir region) is developed, tools of the model of digital integration of the activities of employment services affecting the sphere of implementation of educational products in the form of advanced training and professional retraining programs are proposed. The direction of the study will be relevant for regional government bodies (ministries, departments) in the context of improving employment services as an aggregator implementing state policy in the field of life-long education and decreasing unemployment rate.

In the modern world, one of the most progressive forms of spatial organization, which has the highest concentration of population and economic activity, is an urban agglomeration. Determining the specialization of their economy is a topical issue for regional scientific research. This is due not only to a high concentration of socio-economic activity at the level of these territorial units and the ongoing processes of economic transformations, but also to the choice of those types of economic activity (specializations) that will serve as sources of further development of not only agglomerations, but also the region as a whole. This conditioned the choice of the research objective determining the economic specialization of urban agglomerations (a list of specializing types of economic activity and their complementarity/ intercomplementarity between the core city of the agglomeration and its satellite zone) with a population of less than 500 thousand people, forming around a number of large cities of the Russian Federation (Vologda, Kaluga, Surgut, Khanty-Mansiysk). To achieve the objective, the theoretical aspects of spatial development were studied from the point of view of territorial specialization. The existing approaches to assessing the economic specialization of agglomerations are reviewed and the author's own methodological approach is substantiated; the analysis of spatial dynamics was carried out by assessing the specialization of agglomerations at the mesoand micro levels (based on the use of data on municipalities and data aggregated for specific enterprises of the agglomeration; possible areas for the implementation of projects in the production sector were identified, taking into account complementary (mutually complementary) types of economic activity between the core of the agglomeration and the satellite zone. The information basis of the study consisted of statistical data from Rosstat, data from annual financial statements of enterprises for 2021-2023, collected from the List-Org and Kontur.Fokus databases. The methods of sampling, parsing, analysis and generalization were used. As a result, it was proven that the structure of the economy of the «core» is more complex than that of the satellite zone. In addition, it was found that the Khanty-Mansiysk (as well as the Surgut) agglomeration are highly specialized. At the same time, the most diverse specialization of manufacturing industries is characteristic of the core city of Vologda and Surgut. The research materials may be useful for government bodies in developing strategies, determining development priorities, as well as for representatives of business entities in choosing the location of an enterprise.

The objective of this study is to analyze some of the results of China’s “new urbanization” policy, which is to promote the creation of giant urban clusters chengshiqun 城市群The methodology of the paper includes the analysis of traditional and modern theoretical approaches to the study of urban agglomerations and the phenomenon of megalopolises, as well as a generalization of the results based on practical data. The Chinese urban agglomerations considered in this study are close to the megalopolis, the concept of which was developed in the mid-20th century by Jean Gottmann, but Chinese mega-clusters with a population of 80 to 200 million or more significantly exceed the megalopolis in their scale. China plans to create 19 urban clusters, which will account for nine-tenths of all economic activity in the country. As a result, China will turn into a country of megalopolises – “supercities” in the near future. Three of them already exist and are successfully developing according to the plan: the Greater Bay Area, including Hong Kong and Macau; the Yangtze River Delta, built around Shanghai, and Jingjinji with its center in Beijing. The article notes the features of giant urban clusters in the PRC: their formation occurs with the active direct role of the state, they are characterized by the creation of a single high-speed transport network and a higher degree of integration than Western megalopolises. The creation of large urban agglomerations is considered in this article as a new stimulus for economic growth in the PRC, an attempt is made to analyze the mechanisms of this relationship. According to the author, the unification of Chinese cities into large agglomerations will contribute to the consolidation of markets, growth in productivity, as well as the rapid greening and digitalization of the economy.

One of crucial issues at present is sustainable development of national economy and its industries. For a long time, construction has caused significant damage to the environment. At a certain period the leadership of countries clearly understand that actions are needed to prevent the negative impact. The purpose of the study is to formulate recommendations for strengthening the implementation of the «green» agenda in the housing market adapting foreign experience. To achieve this goal, the authors assess the implementation of «green» agenda in Russia, the main instruments for its financing that have become widespread, and determine what foreign experience can be used by our country to improve the implementation of this agenda. The study analyzes the experience of China, since strengthening cooperation between our countries allows us to consider the areas of interaction in the housing markets within the framework of financing transactions in them. In China, the housing market ensures country’s economic growth which is especially important for Russia. Ensuring cooperation between Russia and China in the field of housing construction and attracting investment in this industry to solve the housing problem seems important. The study is based on such methods as analysis, comparison, synthesis, systematization. The author offers recommendations to increase the efficiency of implementing «green» agenda in Russia, introducing elements of China’s experience in Russian financing transactions in the housing market in the context of ESG transformation. The dissemination of «green» financing of the industry will expand the list of «green» financial instruments for participants in the housing market. This will generally improve the efficiency of Russian developers and will contribute to achieving carbon neutrality.

The purpose of this study is to identify the key areas of readiness for AI use in higher education. The importance of the research is conditioned by rapid digital transformation of higher education under the influence of AI and the lack of a holistic view on the directions of its implementation. The author formulates the following research question: What research directions are observed in the existing discourse concerning the readiness to spread artificial intelligence technologies in higher education? To answer the question, a bibliometric analysis of the combined citations of 2,237 publications indexed in the SCOPUS database for the period of 2015-2025 was carried out. Further, the study carried out qualitative coding of abstracts to narrow the sample to 598 of the most significant publications. Using bibliometric analysis, five main research areas have been identified: (1) formation of strategic readiness of universities for AI in educational organizations (2) formation of organizational readiness of universities for AI (3) formation of consumer readiness of teachers and students for AI (4) formation of psychological parameters of readiness for AI (5) formation of decision-making mechanisms during implementation AI. The results obtained indicate the multilevel nature of the willingness to implement AI on a strategic, organizational, and personal-psychological level. The study is a systematic review of scientific publications using a bibliometric approach, which allows us to identify new promising areas for further development of the topic of higher education readiness for the dissemination of AI. It contributes to understanding the role of AI as a key factor in the transformation of higher education.

The key objective of the study is to reveal the potential for improving the quality of teaching activity in the field of management disciplines based on the application of artificial intelligence (AI) tools. As a methodological approach the authors consider a systemic analysis of transformation of the role and main functions, tasks of a teacher in conditions of rapid development and availability of generative artificial intelligence (AI) tools. The authors substantiate the rise and development of a new hybrid model ‘teacher + AI’ in the field of management disciplines. The paper considers prompt engineering as a tool to develop and design management courses, shows the possibilities of improving evaluation system using artificial intelligence, pays special attention to new tools to visualize learning material to increase students’ involvement, cites successful practices of integrating various AI tools in the learning process. The authors argue that AI not only provides new opportunities to improve teaching activities but also generates new problems of both pedagogical and organizational nature in the field of management disciplines. The findings aim at expanding the discourse on the opportunities and risks of using.