Author page: Alexander Ageev

Alexander I. AgeevAlexander I. Ageev

Director General, Institute for Economic Strategies; President, Russian Division, International League of Strategic Management, Assessment and Accounting; President, Russian Academy of Future Research. Academician, Russian Academy of Natural Sciences; Professor, D.Sc. (Economics), MBA.




Business Models of Technology Solutions

DOI: 10.33917/es-3.201.2025.78-85

The world has entered a period of revolutionary transformations, which are based, among other things, on new technological, engineering and information solutions and business models derived from them.

References:

1. Zasedanie Soveta po nauke i obrazovaniyu pri Prezidente RF [Meeting of the Council for Science and Education under the President of the Russian Federation]. Ofitsial’nyy sayt Prezidenta RF, 2025, 6 fevralya, available at: http://kremlin.ru/events/president/news/76222

2. Vayno A.E., Kobyakov A.A., Saraev V.N. Obraz pobedy [Image of Victory]. Moscow, Institut ekonomicheskikh strategiy RAN, 2012, 140 p.

3. Vayno A.E., Kobyakov A.A., Saraev V.N. Kod rynka [Market Code]. Ekonomicheskie strategii, 2011, no 11, pp. 94–99.

4. Saraev V.N. Bazovye modeli biznes-obolochki: Peterburgskiy ekonomicheskiy forum: GLOWERS-issledovanie global’nykh setey [Basic Models of Business Shell: St. Petersburg Economic Forum: GLOWERS-research of global networks]. 2011, 20 p.

5. Saraev V.N., Chudinova I.A. Biznes-modeli inzhenernykh resheniy: Mezhdunarodnyy nauchno-prakticheskiy inzhenernyy forum “Stroim budushchee vmeste”. Moskva, 4 marta 2025 g. [Business Models of Engineering Solutions: International Scientific and Practical Engineering Forum “Building the Future Together”. Moscow, March 4, 2025]. Moscow, Rossiyskiy dom nauchno-tekhnicheskogo sotrudnichestva.

6. Kuzyk B.N., Ageev A.I., Dobrocheev O.V., Kuroedov B.V., Myasoedov B.A. Rossiya v prostranstve i vremeni [Russia in Space and Time]. Moscow, Institut ekonomicheskikh strategiy RAN, 2004, 335 p.

Digital Platform for Managing Scientific and Technological Development within Economic Cooperation Framework

DOI: 10.33917/es-1.187.2023.56-69

In the circumstances of sanctions-related economic and scientific-technical blockade, the need to build digital mechanisms for managing import substitution processes on the basis of planning and coordinating competencies, similar to the competencies that the USSR State Committee for Science and Technology had, has sharply actualized. It is proposed to use intelligent digital platforms to control the development of science and technology based on the principles of the Soviet information network OGAS and EGSVC projects. The authors of the article substantiate necessity to apply digital information and communication technologies and computing services at various levels of management for innovative agents of any form of ownership through creating and configurating multilayer information-management fields.

References:

1. Ageev A.I., Loginov E.L. Mirovoe soobshchestvo v usloviyakh sverkhkriticheskoi bifurkatsii [The World Community in the Conditions of Supercritical Bifurcation]. Upravlenie slozhnymi organizatsionnymi i tekhnicheskimi sistemami v usloviyakh sverkhkriticheskikh situatsii: Materialy mezhdunarodnoi nauchno-prakticheskoi konferentsii. Moskva, MNIIPU, 21–22 aprelya 2022 g. [Management of Complex Organizational and Technical Systems in Conditions of Supercritical Situations: Proceedings of the International Scientific and Practical Conference. Moscow, MNIIPU, April 21–22, 2022]. Moscow, INES, 2022, pp. 9–12.

2. Ageev A.I., Loginov E.L. Rossiya v novoi ekonomicheskoi real’nosti [Russia in a New Economic Reality]. Moscow, Institut ekonomicheskikh strategii, Assotsiatsiya “Analitika”, 2016, 460 p.

3. Grabchak E.P. Importozameshchenie v energetike Rossii v usloviyakh sanktsii [Import Substitution in the Russian Energy Sector in the Context of Sanctions]. Upravlenie slozhnymi organizatsionnymi i tekhnicheskimi sistemami v usloviyakh sverkhkriticheskikh situatsii: Materialy mezhdunarodnoi nauchno-prakticheskoi konferentsii. Moskva, MNIIPU, 21–22 aprelya 2022 g. [Management of complex organizational and technical systems in conditions of supercritical situations: Proceedings of the international scientific and practical conference. Moscow, MNIIPU, April 21–22, 2022]. Moscow, MNIIPU, 2022, pp. 16–18.

4. Grabchak E.P., Loginov E.L., Chinaliev V.U., Epishkin I.I. Upravlenie razvitiem slozhnykh nauchno-tekhnicheskikh kompleksov na osnove intellektual’nykh tsifrovykh platform (realizatsiya kompetentsii Goskomiteta SSSR po nauke i tekhnike v usloviyakh tsifrovoi ekonomiki) [Managing the Development of Complex Scientific-technical Systems Based on Intelligent Digital Platforms (Implementation of the Competencies of the USSR State Committee for Science and Technology in Conditions of Digital Economy)]. Moscow, INES, 2023, 504 p.

5. Chinaliev V.U. Razvitie politiki importozameshcheniya v promyshlennosti Rossii [Developing Import Substitution Policy in the Russian industry]. Upravlenie slozhnymi organizatsionnymi i tekhnicheskimi sistemami v usloviyakh sverkhkriticheskikh situatsii: Materialy mezhdunarodnoi nauchno-prakticheskoi konferentsii. Moskva, MNIIPU, 21–22 aprelya 2022 g. [Management of Complex Organizational and Technical Systems in Conditions of Supercritical Situations: Proceedings of the International Scientific and Practical Conference. Moscow, MNIIPU, April 21–22, 2022]. Moscow, MNIIPU, 2022.S. 50–53.

Building a Model for Forecasting the Exchange Rate on the Long-term and Short-term Horizons

DOI: 10.33917/es-1.187.2023.16-25

Forecasting the ruble exchange dynamics appears objectively necessary for shaping both the medium-term financial strategy of industry corporations and the general strategic course for occupying leading positions in sectors of business interest, including through the use of new financial instruments, new markets and, in general, a system of strategic planning of socio-economic development of Russia. However, in today’s realities, according to most experts, with whom we cannot but agree, the task of forecasting seems extremely difficult and appears complicated by the fact that the launched crises are unpredictable and are characterized by a diverse nature (pandemic and geopolitical crises, expansion of trade wars and sanctions). In such conditions, when uncertainty grows excessively, it is important to turn to the accumulated experience: to analyze to what extent the available models can be suitable for prospective assessments in the current environment.

References:

[1–15] see No. 6 (186)/2022, p. 25.

16. Ageev A.I., Glaz’ev S.Yu., Mityaev D.A., Zolotareva O.A., Pereslegin S.B. Postroenie modeli prognoza kursa valyut na dolgosrochnom i kratkosrochnom gorizontakh [Building a Model for Forecasting the Exchange Rate on the Long-term and Short-term Horizons]. Ekonomicheskie strategii, 2022, no 6 (186), pp. 16–25, available at: DOI: https://doi.org/10.33917/es-6.186.2022.16-25.

17. Dubrova T.A. Analiz vremennykh dannykh [Time Data Analysis]. Analiz dannykh. Moscow, Yurait, 2019, pp. 397–459.

18. Boks Dzh, Dzhenkins G. Analiz vremennyh ryadov [Time Series Analysis]. Prognozirovanie i upravlenie. Moscow, Mir, 1974, 406 p.

19. Alzheev A.V., Kochkarov R.A. Sravnitel’nyi analiz prognoznykh modelei ARIMA i LSTM na primere aktsii rossiiskikh kompanii [Comparative Analysis of ARIMA and LSTM Forecasting Models on the Example of Russian Companies’ Stocks]. Finansy: teoriya i praktika, 2020, no 24(1), pp. 14–23,
DOI: 10.26794/2587-5671-2020-24-1-14-23.

20. Mhitaryan S.V., Danchenok L.A. Prognozirovanie prodazh s pomoshch’yu adaptivnyh statisticheskih metodov [Sales Forecasting with the Help of Adaptive Statistical Methods]. Fundamental’nye issledovaniya, 2014, no 9-4, pp. 818–822.

21. Pilyugina A.V., Bojko A.A. Ispol’zovanie modelej ARIMA dlya prognozirovaniya valyutnogo kursa [Using ARIMA Models for Exchange Rate Forecasting]. Prikaspijskij zhurnal: upravlenie i vysokie tekhnologii, 2015, no 4, pp. 249-267.

22. Ruppert D., Matteson D.S. Statistics and Data Analysis for Financial Engineering. Springer, 2015, available at: https://link.springer.com/book/10.1007%2F978-1-4939-2614-5.

23. Garcia F., Guijarro F., Moya I., Oliver J. Estimating returns and conditional volatility: A comparison between the ARMA-GARCH-M models and the backpropagation neural network. International Journal of Complex Systems in Science, 2012, no 1(2), pp. 21–26.

24. Maniatis P. Forecasting the Exchange Rate Between Euro And USD: Probabilistic Approach Versus ARIMA And Exponential Smoothing Techniques. Journal of Applied Business Research (JABR), 2012, no 28(2), pp. 171–192, available at: https://doi.org/10.19030/jabr.v28i2.6840.