ІМІТАЦІЙНЕ МОДЕЛЮВАННЯ СКЛАДНИХ СИСТЕМ

Abstract

The article examines simulation modeling of complex systems. Simulation modeling offers a means to replicate real-world processes in a controlled virtual environment, providing insights into system dynamics, interactions, and behaviors that may be difficult or impractical to observe directly. With the rapid advancement of computational technologies, simulation models have become increasingly sophisticated, allowing for more accurate and detailed representations of complex systems. In today's interconnected and rapidly changing world, decision-makers in business, healthcare, finance, environmental science, and many other sectors rely on simulation modeling to analyze scenarios, evaluate strategies, and make informed decisions. It serves as a valuable tool for forecasting outcomes, optimizing processes, and identifying potential risks and opportunities. Moreover, simulation modeling plays a crucial role in innovation and research by providing a platform for hypothesis testing, data analysis, and validation of theoretical concepts. It facilitates the exploration of new ideas, designs, and strategies in a risk-free virtual environment before implementation in the real world. Additionally, the educational significance of simulation modeling cannot be overstated. It offers students and researchers a hands-on approach to learning complex concepts, enhancing their understanding and practical skills in various disciplines. By engaging with simulation models, learners can develop critical thinking, problem-solving, and analytical skills essential for success in today's competitive landscape. It is noted that simulation modeling, in particular machine simulation, is widely used in the study of complex systems due to a number of advantages. These advantages include the ability to quickly obtain information at the early stages of the preliminary design of systems, the study of conditions that are not amenable to full-scale experiments, the prediction of system behavior, the reduction of time for testing simulation models, the acquisition of a large amount of information about real processes, and the solution of optimization tasks of economic systems. This approach makes it possible to solve tasks that are often impossible or very difficult to perform with the help of other research methods.