(untitled) (Journal of Economic Research & Business Administration, 2024)
In this study, models of artificial neural networks of migration balances are developed in order to improve the efficiency of management of socio-economic development of single-industry towns in the Republic of Kazakhstan. Now there are no universal tools for forecasting indicators of socio-economic development in general and characterising migration processes in particular. However, the volume of budget allocations to address human resources issues in single-industry towns, the creation of social facilities and the implementation of other activities that are significant for economic, social and infrastructural development, the direction of development of single-industry towns depend on the forecast of migration balance. In addition, the forecast of migration balance is important for identifying core areas and their subsequent priority development. In this article, a substantial analysis of researchers’ works is carried out, and it is determined that artificial intelligence models, in particular, the most adaptive neural networks are not used in forecasting the migration balance. The purpose of this study is to develop models of artificial neural networks of migration balance to improve the efficiency of management of socio-economic development of single-industry towns in the Republic of Kazakhstan. The result of the research is a methodological approach and a toolkit for forecasting the migration balance for single-industry towns in the Republic of Kazakhstan. The developed approach to forecasting and the toolkit are universal in the field of forecasting socio-economic indicators. In addition, the results described in the article can be used in other studies in the field of forecasting and planning. In particular, the developed toolkit can be used to assess the effectiveness of management decisions, for example, in the implementation of evidence-based policy for the development of single-industry towns. Key words: migration balance, single-industry towns, artificial intelligence models, artificial neural networks, model error, management of socio-economic development.