Publisher's Synopsis
The Beginner's Introduction to Agent-Based Modeling offers a clear, practice-oriented guide to the foundational concepts, methodologies, and real-world applications of ABM for researchers, analysts, and modelers across disciplines. Written by an experienced computational social scientist, this book bridges theory with implementation, helping readers build reliable, transparent, and reproducible agent-based models using proven strategies and open-source tools.
Designed for early-career scholars, applied researchers, and advanced students, the book provides a structured pathway from conceptual modeling to code development, calibration, and model evaluation. Core topics include agent behavior specification, environmental dynamics, stochastic processes, model validation, sensitivity analysis, and best practices in documentation and transparency. Emphasis is placed on reproducibility, stakeholder communication, and the integration of ABM with emerging technologies such as machine learning and high-performance computing. Key features include: Clear explanations grounded in real-world case studies.Practical coding templates using Python (Mesa) and NetLogo.
Annotated examples that illustrate the modeling lifecycle.
Glossary, sample model code, and curated resources for continued learning. Whether you're developing a public policy simulation, ecological model, or organizational behavior system, this book equips you with the tools and insights to design meaningful, data-informed simulations in today's complex systems landscape.
