Fattakhov R.V.Institute for Regional Research and Spatial Development of Financial University under Government of Russian Federation, Moscow, Russian Federation fattakhov@mail.ru
Importance We all live in a complex, ever-changing world. To model the socio-economic and spatial processes and phenomena in the high level of detail using traditional approaches is becoming increasingly difficult. Due to the rapid development of computer technology, an agent-based approach has come into service in the classical methods and modeling tools. This approach allows to model complex systems, the state of which changes since the emergence of interaction between agents. Objectives The purpose of this paper is to analyze in detail and describe new tools to build complex systems, i.e. an agent-based approach supporting the principle of modeling "bottom-up", the activities of independent agents at the microlevel and the performance at the macrolevel, and to consider in detail such concepts as an agent, environment model, and stages of model design. Methods This approach is widely used in many areas covering social, physical and biological aspects of human life: aviation, medicine, the military direction, ecology, market analysis, supply chain, forecasting the spread of epidemics, the solution of transport problems, issues of social segregation, urban, regional, and countries' development, as well as in many other areas. The article describes the essence of the agent, its basic and additional properties and attributes, rules of behavior and interaction with other agents and with the agent-based model's environment. As well, the article describes the model's environment and the possibilities of using GIS to create it. The paper considers the possibility of using specific databases and systems, such as CRM-system, ERP-systems, and HR as a base for building agent-based models. Results The study gives a system description of the agent-based approach; it analyzes the applied aspects of program implementation, considers the principles of horizontal and hierarchical organization of models, shows the possibilities of use of supercomputers and cloud computing services when scaling agent-based models, identifies limitations of this approach, and gives recommendations on its applications. Conclusions and Relevance The authors conclude that this approach is a useful tool to simulate complex objects, such as urban systems. It can complement the classical methods of modeling and forecasting, allowing acquiring new knowledge.
Keywords: agent-based model, multi-agent system, social modeling, cellular automaton, agent, habitat, Geographic Information System, supercomputer
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