NEW GENERATION STRATEGIC PLANNING: ARTIFICIAL INTELLIGENCE AS A TOOL FOR IMPROVING THE EFFICIENCY OF MANAGING THE SOCIO-ECONOMIC DEVELOPMENT OF SINGLE-INDUSTRY TOWNS (BASED ON THE MATERIALS OF THE REPUBLIC OF KAZAKHSTAN)

УДК 332.144:004

  • S. I. Mezhov Altai State University (Barnaul, Russia) Email: megoff@mail.ru
  • I. V. Mishchenko Altai State University (Barnaul, Russia) Email: mis.iv@mail.ru
  • M. G. Krayushkin Altai State University (Barnaul, Russia) Email: kramaks-97@mail.ru
  • N. B. Shurenov RMIT University (Melbourne, Australia) Email: nursultan.shurenov@rmit.edu.au
DOI_disc_logo https://doi.org/10.14258/epb202524
Keywords: strategic planning, forecasting, socio-economic development, model error, migration balance, artificial intelligence models, single-industry town

Abstract

The results of a study the development forecasting the tools the balance on devoted of migration to artificial intelligence technology in order to improve the effectiveness of strategic planning for the socio-economic development of single-industry towns in the Republic of Kazakhstan.

Currently, there are no universal methodologies for forecasting socio-economic indicators and characteristics of migration processes. At the same time, the amount of budget funds allocated for human resources, social infrastructure and measures that determine the trajectory of the development of single-industry towns directly depends on the accuracy of the forecast of the migration balance.

This article provides an analytical review of existing research in the field of forecasting migration processes, which revealed the lack of representation of artificial intelligence models, in particular adaptive neural networks, in this area.

The purpose of the study is to develop tools for predicting migration balances based on artificial intelligence technology in order to improve the effectiveness of strategic planning for the socio-economic development of single-industry towns in the Republic of Kazakhstan. The result of the study is a methodological approach and tools for forecasting the migration balance, applicable in the context of single-industry towns of the Republic of Kazakhstan. The proposed approach and tools are characterized by potential versatility in the field of forecasting socio-economic indicators. The described results can be used in further research in the field of forecasting and planning, as well as to evaluate the effectiveness of management decisions, in particular, in the implementation of evidence-based policies focused on the development of single-industry towns.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Author Biographies

S. I. Mezhov, Altai State University (Barnaul, Russia)

доктор экономических наук, профессор кафедры финансов и кредита

I. V. Mishchenko, Altai State University (Barnaul, Russia)

кандидат экономических наук, доцент кафедры региональной экономики и управления

M. G. Krayushkin, Altai State University (Barnaul, Russia)

ассистент кафедры экономики и эконометрики

N. B. Shurenov, RMIT University (Melbourne, Australia)

приглашенный лектор

References

Ахметова З., Товма Н., Шуренов Н. Анализ демографических тенденций моногородов Восточно-Казахстанской области // Научный журнал. 2023. № 3 (107). С. 85-99.

Тургель И. Д., Божко Л. Л., Сюй Л. Государственная поддержка развития моногородов России и Казахстана // Вестник Финансового университета. 2016. № 2. С. 22-32.

Моногорода Казахстана: программы принимаются, проблемы не решаются // Ритм Евразии. 2023. URL: https://dzen.ru/a/Y75-toE8Eg_tY_nj (дата обращения: 25.12.2024).

Whelpton P. K. An empirical method of calculating future population // Journal of the American Statistical Association. 1936. № 31. Pp. 457-473.

Новосельский С. А. Демография и статистика. М., 1978. 272 с.

Вишневский А. Г. Воспроизводство населения и общество: История, современность, взгляд в будущее. М., 1982. 287 с.

Вишневский А. Г. Демографическая модернизация России. М., 2006. 608 с.

Вишневский А. Г., Андреев Е. М., Трейвиш А. И. Перспективы развития России: роль демографического фактора. М., 2003. 59 с.

Цыбатов В. А. Цифровые технологии прогнозирования и стратегического планирования регионального развития: теория и практика // Вестник Самарского государственного экономического университета. 2023. № 6 (224). С. 69-83.

Бюро национальной статистики Агентства по стратегическому планированию и реформам Республики Казахстан. URL: http://www.taldau.stat.gov.kz (дата обращения: 01.02.2025).

Pattanayak S. Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python: textbook. Apress, 2019. 480 с.

Bernard M. Artificial Intelligence in Practice: textbook. Wiley, 2019. 605 с.

Метод BFGS или один из самых эффективных методов оптимизации. URL: https://habr.com/ru/post/333356/ (дата обращения: 05.02.2025).

Mezhov S., Krayushkin M. Comparative Analysis of Methods of Forecasting the Consumer Price Index for Food Products (on the Example of the Altai Territory) // Proceedings of International Conference on Applied Innovation in IT. 2022. 10 (1). Pp. 119-124.

Mishchenko I. V., Mishchenko Val. V. Differentiated Approach to Functioning and Development of SingleIndustry Towns in Kazakhstan // The Journal of Economic research and Business administration. 2023. Vol. 3. Pp. 74-83.

REFERENCES

Akhmetova Z., Tovma N., Shurinov N. Analysis of demographic trends in single-industry towns of the East Kazakhstan region. Scientific Journal. 2023. No. 3 (107). Pp. 85-99.

Turgel I. D., Bozhko L. L., Xu L. State support for the development of single-industry towns in Russia and Kazakhstan. Bulletin of the Financial University. 2016. No. 2. Pp. 22-32.

Monotowns of Kazakhstan: programs are accepted, problems are not solved. Rhythm of Eurasia. 2023. URL: https://dzen.ru/a/Y75-toE8Eg_tY_nj (date of access: 25.12.2024).

Whelpton P. K. An empirical method of calculating future population. Journal of the American Statistical Association. 1936. No. 31. Pp. 457-473.

Novoselsky S. A. Demography and statistics. Moscow, 1978. 272 p.

Vishnevsky A. G. Reproduction of the population and society: History, modernity, a look into the future. Moscow, 1982. 287 p.

Vishnevsky A. G. Demographic modernization of Russia. Moscow, 2006. 608 p.

Vishnevsky A. G., Andreev E. M., Treyvish A. I. Prospects for the development of Russia: the role of the demographic factor. Moscow, 2003. 59 p.

Tsybatov V. A. Digital technologies of forecasting and strategic planning of regional development: theory and practice. Bulletin of Samara State University of Economics. 2023. No. 6 (224). Pp. 69-83.

Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan. URL: http://www.taldau.stat.gov.kz (date of access: 01.02.2025).

Pattanayak S. Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python: textbook. Apress, 2019. 480 p.

Bernard M. Artificial Intelligence in Practice: textbook. Wiley, 2019. 605 p.

The BFGS method or one of the most effective optimization methods. URL: https://habr.com/ru/post/333356 / (date of access: 05.02.2025).

Mezhov S., Krayushkin M. Comparative Analysis of Methods of Forecasting the Consumer Price Index for Food Products (on the Example of the Altai Territory). Proceedings of International Conference on Applied Innovation in IT. 2022. 10 (1). Pp. 119-124.

Mishchenko I. V., Mishchenko Val. V. Differentiated Approach to Functioning and Development of SingleIndustry Towns in Kazakhstan. The Journal of Economic research and Business administration. 2023. Vol. 3. Pp. 74-83.

Published
2025-06-11
How to Cite
1. Mezhov S. I., Mishchenko I. V., Krayushkin M. G., Shurenov N. B. NEW GENERATION STRATEGIC PLANNING: ARTIFICIAL INTELLIGENCE AS A TOOL FOR IMPROVING THE EFFICIENCY OF MANAGING THE SOCIO-ECONOMIC DEVELOPMENT OF SINGLE-INDUSTRY TOWNS (BASED ON THE MATERIALS OF THE REPUBLIC OF KAZAKHSTAN) // Economics Profession Business, 2025. № 2. P. 63-73. URL: https://journal.asu.ru/ec/article/view/epb202524.
Issue
No 2 (2025): Economics Profession Business
Section
ЭКОНОМИЧЕСКИЕ НАУКИ

Economics Profession Business is a golden publisher, as we allow self-archiving, but most importantly we are fully transparent about your rights.

Authors may present and discuss their findings ahead of publication: at biological or scientific conferences, on preprint servers, in public databases, and in blogs, wikis, tweets, and other informal communication channels.

Economics Profession Business (EPB) allows authors to deposit manuscripts (currently under review or those for intended submission to EPB) in non-commercial, pre-print servers such as ArXiv.

Authors who publish with this journal agree to the following terms:

 

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).