Aggregated Agent-Based Simulation Model of Migration Flows of the European Union Countries

Makarov, Valeriy; Bakhtizin, Albert; Beklaryan, Gayane; Akopov, Andranick; Rovenskaya, Elena; Strelkovskiy, Nikita

doi:10.31857/s042473880004044-7

Cited by 7 publications

(9 citation statements)

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“…At the same time, new data and tools are enhancing and reinvigorating this classic spatial analysis framework, providing the impetus for a new era of spatial interaction. Some examples include: (1) the availability of more representative samples and time-aware flows (Waddington et al, 2019;Oshan, 2020;Park et al, 2021); (2) the use of machine learning techniques to capture spatial, temporal, and network dependencies (Ren et al, 2019;Yeghikyan et al, 2020;Yang et al, 2020); (3) connecting SI models and agent-based modeling methods (Kowalski, 2019;Makarov, 2019;Ge et al, 2021); and (4) incorporating multiple scales of analysis (Thompson et al, 2019;McCulloch et al, 2021). These nascent trends hold promise for building upon the rich traditions of SI modeling presented here by combining data-driven and theory-driven modeling approaches.…”

Section: Discussionmentioning

confidence: 99%

Spatial interaction modeling

2022

Handbook of Spatial Analysis in the Social Sciences

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The concept of spatial interaction (SI) encapsulates the domain of human activities that occur between a set of locations embedded within geographical space. Data about such processes are essential for studying a wide spectrum of geographic phenomena that are important to society, such as the accessibility of services, product demand, transportation trends, and demographic dynamics. In particular, SI models seek to explore, explain, and predict aggregate movements or flows that occur across an abstract or physical network, which can be useful on its own, as well as a factor within other regional models. As the number and nature of SI modeling applications have grown, the associated theory and tools have simultaneously evolved to consider more complex spatial relationships, resulting in numerous expansions of the modeling paradigm. In this chapter, some foundations of SI modeling are first laid out before presenting a simple demonstration and then describing several extensions to the core modeling methodology.

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Section: Discussionmentioning

confidence: 99%

Spatial interaction modeling

2022

Handbook of Spatial Analysis in the Social Sciences

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“…The model provides for an inflow of migrants with the subsequent 'transformation' of arriving agents into indigenous people after the period required for assimilation has expired (1-30 years). Immigration is mainly due to the 'gravity effect' [10], which sets a reinforcing feedback between the number of available non-assimilated migrants and the inflow intensity of new agents. At the same time, in the model, the share of new immigrants (of the number of existing ones), as well as the costs of education and integration, are the key control parameters.…”

Section: General Description Of the Modelmentioning

confidence: 99%

“…The model of 'nomads and farmers' [2,3], in which some agents ('conditional farmers') create resources, while others destroy them in order to expand personal space ('conditional nomads') should be noted. Also, the models of population segregation of the Schelling class [4,5], models of movement of an ensemble of unmanned vehicles [6,7], models of migration and demographic processes [8][9][10], etc. are well known.…”

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confidence: 99%

Simulation of migration and demographic processes using FLAME GPU

Makarov¹,

Bakhtizin²,

Beklaryan³

et al. 2022

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This article presents an approach to modeling migration and demographic processes using a framework designed for large-scale agent-based modeling – FLAME GPU. This approach is based on the previously developed simulation model of interaction between two communities: migrants and natives that is implemented in the AnyLogic simulation software. The model has had a low dimensionality of the discrete space representing the operating environment of the agent populations and a deterministic decision-making system of each agent. At the same time, the presence of multiple interactions between agents and transitions between their states determines a high computational complexity of such a model. The use of FLAME GPU makes it possible to conduct extensive simulation experiments with the model, mainly due to the parallelization of computational processes at the level of each agent, as well as the implementation of the mechanism of multiple computations using Monte Carlo techniques. The developed framework is used to study the impact of the most important parameters of the model (e.g., rate of migration, governmental expenditures on integration, frequency of creation of new workplaces, etc.) on the key outputs of the modeled socio-economic system (in particular, population size, share of migrants, number of assimilated migrants, GDP growth rate, etc.). The proposed approach can be used to develop decision-making systems for planning the hiring of new employees based on the forecast dynamics of migration and demographic processes.

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“…A sim-ilar approach is applicable to many objects of life as lifeless nature. There are examples of the development of simulation models for the simulation of complex socio-economic systems [4,5], vertically integrated petroleum companies and financial corporations [6], ecological and business systems [7,8], modeling human crowd behavior in emergencies [9,10] and some others. In this study, various methods of simulation modelling are used, in particular, the methods of system dynamics, agent-based and discreteevent modelling, which are supported in Powersim, AnyLogic, etc.…”

Section: Introductionmentioning

confidence: 99%

Developing digital twins for production enterprises

Makarov¹,

Bakhtizin²,

Beklaryan³

2019

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This article presents a new approach to developing digital twins of production companies with the use of simulation methods. It describes the concept of digital twins as an integrated system that aggregates simulation models, databases and intelligent software modules of the class of genetic optimization algorithms, subsystems of data mining, etc. The article presents examples of simulation models of different production companies, in particular, a typical assembly plant and a typical oil production enterprise. The first company carries out activities to assembly products from individual components with its own individual characteristics. To describe the behavior of such an enterprise, methods of agent and discrete-event modeling are used. The second enterprise produces raw carbohydrate materials at existing fields with individual characteristics. The integrated simulation models thus developed are integrated with a subject-oriented database and optimization modules that facilitate providing a control of the technological and resource characteristics of the respective production enterprises. The development of these models was performed using AnyLogic and Powersim simulation systems that support agent-based modeling and system dynamics methods. We demonstrate here the possibility of creating 'digital twins' for production companies using modern simulation tools.

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Aggregated Agent-Based Simulation Model of Migration Flows of the European Union Countries

Cited by 7 publications

References 0 publications

Spatial interaction modeling

Spatial interaction modeling

Simulation of migration and demographic processes using FLAME GPU

Developing digital twins for production enterprises

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