computersimulated

Computer-simulated, or computer simulation, refers to the use of computer models to imitate real-world systems or processes. A computer simulation implements a mathematical or logical model in software and runs it to observe how the system behaves under specified conditions. The purpose is to study phenomena that are difficult to observe directly, costly to experiment with, or dangerous to test in the real world, and to explore outcomes under varying assumptions.

Common methods include agent-based simulations, which model individual entities and their interactions; discrete-event simulations, which track the timing of discrete occurrences; continuous simulations, which solve differential equations to represent changing quantities; and Monte Carlo simulations, which use repeated random sampling to estimate distributions or probabilities. Models are built from theory, data, and simplifying assumptions, then calibrated and validated against empirical observations to improve accuracy.

History and development began in earnest with the rise of digital computers in the mid-20th century. Early work in operations research, physics, and meteorology demonstrated the value of simulations for complex systems. The Monte Carlo method, developed by Stanislaw Ulam and John von Neumann, popularized stochastic simulation. Since then, computer simulation has become integral to science, engineering, finance, and public policy, as well as film and video game production for generating realistic environments and physics.

Applications span climate modeling, fluid dynamics, structural analysis, traffic and logistics, epidemiology, financial risk assessment, product design, and training simulations. Benefits include safer testing, cost reduction, and rapid scenario exploration, while limitations arise from model inaccuracies, uncertainty, computational demands, and the risk of misinterpretation without proper verification and validation.