СУТНІСТЬ ІМІТАЦІЙНОГО МОДЕЛЮВАННЯ

Abstract

The article examines the issue of the essence of simulation modeling. As our world grows increasingly complex, understanding and managing intricate systems become more challenging. Imitative modeling offers a way to simplify and study these systems, providing valuable insights into their behavior and interactions. Advancements in technology, particularly in computing power and simulation software, have made imitative modeling more sophisticated and accessible. This technological progress enables more accurate and detailed simulations, enhancing the value and applicability of modeling across various fields. Imitative modeling provides a tool to simulate different scenarios, evaluate options, and optimize outcomes, thus supporting better decision-making processes. Furthermore, imitative modeling drives innovation by identifying new approaches or configurations that optimize performance. Whether it's designing a new product, improving a manufacturing process, or optimizing a supply chain, modeling can help find the best solutions to complex problems. Addressing global challenges such as climate change, environmental sustainability, public health, and urban planning requires informed strategies and policies. Imitative modeling can play a crucial role in this by simulating the impact of different interventions or policies, thereby informing better approaches to tackle these pressing issues. Additionally, imitative modeling serves as a valuable educational tool, helping students understand complex concepts and theories through practical application. It also supports research by providing a framework for hypothesis testing, data analysis, and validation. It is noted that the widespread use of simulation modeling became a reality at a certain stage of the development of information technologies, which include means of collecting, transmitting, processing and storing information. These means include not only computers, but also computational mathematics tools, multi-level programming tools, database management systems, data banks, and others. The simulation model is a complex mathematical and algorithmic structure, which is intended for the study of a certain system. The method, which is based on the creation and analysis of simulation models, is called "machine simulation" or "simulation modeling".