Introduction to Computer Simulation and Complex Systems Modeling

Understanding complex real-world systems—from disease propagation to resource allocation—requires powerful analytical tools. Simulation modeling allows you to create simplified digital environments to test hypotheses and predict emergent behaviors. In this text-based course, you will learn how to design, build, and analyze computational models to study real-world dynamic processes. What you will learn: Understand core concepts of simulation theory, agent-based modeling, and complex dynamic systems. Build multi-agent simulations using NetLogo to observe dynamic pattern formation. Model operational processes and resource constraints with discrete-event modeling tools like Arena. Apply stochastic techniques and Monte Carlo methods to handle randomness and uncertainty. Analyze generated simulation data and statistical outcomes using data analysis tools like Orange. Evaluate scenarios involving epidemics, queue management, and social dynamics. The course starts with essential terminology, basic definitions, and theoretical foundations before guiding you through structured written explanations and model construction logic. Designed for absolute beginners, this program requires no prior coding or math background. Start reading today to unlock the fundamentals of computational modeling.