SIMULATION MODELING AND ANALYSIS IN APPLIED ECONOMICS

Анотація

The article presents a comprehensive study of the application of simulation modeling in applied economics and systematizes the main approaches to its implementation. Particular attention is paid to analyzing the potential of simulation modeling for studying complex economic systems, forecasting the dynamics of economic processes, and supporting strategic decision-making across various sectors of the economy. Discrete-event modeling, agentbased modeling, system dynamics, and hybrid approaches, which combine the advantages of multiple methods to achieve a comprehensive and integrated analysis, are examined. It has been established that discrete-event modeling is effective for accurately reproducing operational processes and the sequence of events, agent-based modeling allows for assessing the behavior of individual economic agents and identifying emergent effects and nonlinear interactions, while system dynamics ensures the forecasting of long-term macroeconomic trends and the evaluation of feedback loops between sectors. Hybrid models demonstrate high practical value by integrating micro- and macro-level analyses, enabling a more precise assessment of complex scenarios for economic system development. The article emphasizes that simulation modeling contributes to enhancing the efficiency of managerial decisions, optimizing resource utilization, and increasing the adaptability of strategies in a dynamic and uncertain economic environment. Furthermore, the potential integration of modern digital technologies, such as big data and artificial intelligence, is highlighted as a means of expanding modeling capabilities, improving forecast accuracy, and providing real-time decision support. The study demonstrates that simulation modeling is not only an effective tool for analytical evaluation of economic processes but also a strategic mechanism that advances both economic science and management practice, enabling a holistic and multi-dimensional understanding of economic systems, facilitating more informed and robust decision-making, and supporting the development of adaptive, forward-looking strategies in highly dynamic and uncertain contemporary economic conditions.

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