The book is structured in two parts, covering both discrete and continuous time frameworks. Part I focuses on discrete time, introducing foundational concepts such as stochastic optimal control theory and numerical dynamic programming. It covers key computational techniques, including value function iteration, the endogenous gridpoint method, and methods for handling inequality constraints. These tools are then extended to heterogeneous agent models, exploring their mechanics, the law of motion of the agents' distribution, stationary equilibria, transition dynamics, and aggregate risk. Notable models, such as Huggett (1993), Aiyagari (1994), and Krusell-Smith (1998), are thoroughly examined and solved with step-by-step numerical algorithms and visualizations.

Part II transitions to continuous time, enabling the incorporation of more sophisticated stochastic processes. Topics include dynamic programming in continuous time, diffusion and jump diffusion processes, and the numerical methods-such as finite upwind difference schemes-needed to solve these models.

With a step-by-step approach, this textbook bridges the gap between representative and heterogeneous agent models, providing clear visualizations, numerical algorithms, and solution techniques. Readers will gain not only the computational skills to implement these models but also the insight to select the appropriate framework for their research objectives.