Abstract
The paper is devoted to the development of a methodology for forecasting migration processes. It is proposed to integrate probabilistic methods of study of migration flows with simulations, each of which has its advantages and disadvantages. The joint use of such approaches will allow, on the one hand, to ensure the accuracy of modeling in accordance with the quality of the source data, and on the other – to analyze the studied system at the required level of abstraction. The results of approbation of the proposed combination of analytical entropy and simulation models of migration are given on the example of studying the migration processes of the Arctic countries of Eurasia.
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Acknowledgements
The study was financially supported by RFBR project № 16-29-12878 (ofi_m) “Development of methods for identification of dynamic models with random parameters and their application to forecasting migration in Eurasia”.
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Bystrov, V.V., Shishaev, M.G., Malygina, S.N., Khaliullina, D.N. (2019). Forecasting of Migration Processes by Integrating Probabilistic Methods and Simulation. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Computational Statistics and Mathematical Modeling Methods in Intelligent Systems. CoMeSySo 2019 2019. Advances in Intelligent Systems and Computing, vol 1047. Springer, Cham. https://doi.org/10.1007/978-3-030-31362-3_34
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