Agent-Based Approach for Modelling the Labour Migration from China to Russia
DOI:
https://doi.org/10.17059/2017-2-1Keywords:
agent-based modelling, software, demography, international migration, labour migration, types of population reproduction, population size of the region, age and gender structure of the population, forecasting, Russian-Chinese relationsAbstract
The article describes the process of labour migration from China to Russia and shows its modelling using the agent-based approach. This approach allows us to simulate an artificial society in a computer program taking into account the diversity of individuals under consideration, as well as to model a set of laws and rules of conduct that make up the institutional environment in which the members of this society live. A brief review and analysis of agent-based migration models presented in the foreign literature are given. The agent-based model of labour migration from China to Russia developed by the Central Economic Mathematical Institute of the Russian Academy of Sciences simulates human behaviour close to reality, which is based on their internal purposes, determining the agents choice of territory as a place of residence. Therefore, at the development of the agents of the model and their behaviour algorithms, as well as the organization of the environment in which they exist and interact, the main characteristics of the population of two neighbouring countries and their demographic processes have been considered. Using the model, two experiments have been conducted. The purpose of the first of them was to assess the effect of depreciation of the rubble against the yuan on the overall indexes of labour migration, as well as its structure. In the second experiment, the procedure of the search of the information by agents for the migratory decision-making was changing. Namely, all generalizing information on the average salary by types of activity and skill level of employees, both in China and Russia, became available to all agents irrespective of their qualification level.References
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Copyright (c) 2017 Valeriy Leonidovich Makarov, Albert Raufovich Bakhtizin, Elena Davidovna Sushko, Alina Fagimovna Ageeva
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