Agent-Based Modeling and Simulation with NetLogo

Understanding how individual interactions shape complex real-world systems is key to modern scientific and social analysis. This text-based course introduces you to the fundamentals of agent-based modeling (ABM) using NetLogo, the premier platform for simulating decentralized systems. You will transition from understanding basic system dynamics to writing custom NetLogo code, defining agent behaviors, and running systematic experiments. By reading through clear explanations and studying practical code examples, you will gain the skills to build simulations that model ecological balances, economic markets, and social networks. What you'll learn: - Understand the foundational concepts of agent-based modeling and decentralized systems. - Master the NetLogo programming language, including turtles, patches, links, and observer contexts. - Design custom simulation environments to model real-world interactions in ecology, sociology, and economics. - Configure model parameters and run systematic experiments using BehaviorSpace. - Apply data export techniques to analyze simulation results using modern data analysis workflows. - Analyze simulation behaviors to identify emergent patterns and system tipping points. The course begins with core terminology and the theory of complex systems before guiding you step-by-step through NetLogo syntax, agent logic, and model execution. You will study complete code templates and learn how to run multi-run experiments to validate your hypotheses. Designed for beginners in simulation, data science, or social research, with no prior programming experience required. Start reading today to unlock the power of agent-based simulation and discover how simple rules create complex behaviors.