Introduction to Complex Systems Modeling with StarLogo

How do traffic jams form without an accident? How do epidemics spread through a city? Understanding these complex, decentralized systems requires a shift from traditional linear thinking to agent-based modeling. This written course introduces you to the fundamentals of complex systems and simulation using StarLogo. You will transition from a beginner to a confident modeler, capable of designing, running, and analyzing simulations that reveal how individual behaviors create large-scale emergent patterns. What you'll learn: 1. Understand the core principles of complex systems, feedback loops, and emergent behavior. 2. Program individual agents and environment patches using StarLogo's block-based language. 3. Design simulations to model real-world phenomena like traffic flow, disease spread, and resource consumption. 4. Analyze simulation data to identify critical thresholds and system tipping points. 5. Apply decentralized thinking to solve problems in urban planning, ecology, and social sciences. 6. Implement best practices for calibrating, testing, and documenting your simulation models. You will start with foundational definitions of complex systems before moving step-by-step through StarLogo's programming logic. Through structured reading and written code exercises, you will learn to build and refine several simulation models from scratch. This course is designed for beginners in data science, urban planning, or complex systems, with no prior programming or modeling experience required. Start reading today to unlock the power of simulation and understand the hidden patterns of our complex world.