This chapter introduces the fundamental principles of nonlinear dynamics and their relevance to economic systems. While linear models assume predictable and proportional relationships, nonlinear dynamics capture the reality of economic behavior such as sudden regime shifts, instability, high sensitivity to initial conditions, and emergent patterns arising from interconnected components. Key theoretical concepts, including chaos, bifurcations, strange attractors, and stability analysis, are presented alongside their economic interpretations. The chapter explores how stochastic differential equations can model economic systems subject to random shocks and demonstrates computational techniques for analyzing these systems using Euler and Runge-Kutta numerical methods. You will learn to detect chaos using Lyapunov exponents, assess system stability, and simulate dynamic economic scenarios. Each concept is grounded in practical implementation using Python, with working code for simulation, visualization, and optimization. By combining mathematical rigor with hands-on application, this chapter establishes the analytical foundation for understanding complex economic dynamics explored throughout the book.