May 1, 2024
Updated May 9, 2025
24 minute read
Agent-Based Modeling (ABM) is a computational approach used to simulate the actions and interactions of autonomous entities, known as agents, within a defined environment. The goal is to understand how the collective behavior of these agents, each following a set of simple rules, can lead to complex, system-level patterns and outcomes. This "bottom-up" methodology allows researchers and analysts to explore how individual behaviors can generate large-scale phenomena that might not be obvious from examining the components in isolation. You might find it fascinating to see how simple individual actions can cascade into intricate and sometimes surprising systemic results, much like observing how a flock of birds moves in unison without a central leader.
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Find a path to becoming a Agent-Based Modeling. Learn more at:
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Reading list
We've selected 18 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Agent-Based Modeling.
Is widely considered a standard textbook for learning ABM, particularly for beginners. It provides a comprehensive introduction to the core concepts, methods, and applications of ABM with a hands-on approach using NetLogo, a popular modeling environment. It's excellent for gaining a broad understanding and solidifying foundational knowledge, making it suitable for high school, undergraduate, and even graduate students new to the field.
Published recently in 2023, this book offers an introduction to ABM specifically for social scientists. It integrates computational ABMs with classic mathematical approaches and provides numerous examples in NetLogo. This is an excellent resource for those interested in modeling social dynamics and cultural evolution.
This textbook provides a practical introduction to designing, implementing, and analyzing agent-based models, with a strong emphasis on analysis and design issues. It uses NetLogo and includes numerous examples and exercises. It's well-suited for students and researchers who want to develop a solid understanding of the practical aspects of ABM.
Specifically addresses agent-based modeling within the field of economics. It highlights the benefits of ABM for handling complexity in economic systems and provides practical advice on model design and creation. It includes examples in NetLogo and is suitable for students and researchers in economics.
Provides a strong theoretical foundation for understanding complex adaptive systems, of which ABM key modeling technique. It explains the 'why' behind using ABM to study social phenomena and is highly recommended for gaining a deeper conceptual understanding.
This classic book, while not strictly about ABM, is considered a foundational text that inspired the development of agent-based models, particularly in social sciences. Schelling's models, like the segregation model, demonstrate how simple individual behaviors can lead to complex宏观 patterns. It's essential reading for understanding the motivation and intuition behind ABM.
This is the first ABM textbook specifically designed for researchers in archaeology and related social sciences. It offers a modular approach to learning and applying ABM to understand past human societies. It includes examples of how ABM has been applied in archaeological research.
Provides a comprehensive treatment of graphs, networks, and ABM in ecology and environmental sciences. It's aimed at researchers, teachers, and students and includes practical examples and code. It's a good resource for those applying ABM to ecological problems.
Offers a concise introduction to ABM, suitable for social scientists and those new to computational modeling. It covers the basic concepts, design, and practical issues in building ABMs. While not a comprehensive textbook, it's a great starting point for understanding the potential of ABM in various disciplines.
While focusing on multiagent systems from a computer science perspective, this book covers fundamental concepts of agents and their interactions that are highly relevant to ABM. It delves into the design and theory of intelligent agents. It's particularly useful for those with a computer science background or interest in the computational aspects of ABM.
This edited volume explores the integration of GIS and ABM for simulating social and ecological processes. It provides a review of theory and methods and is relevant for researchers interested in spatially explicit ABMs. While older, it offers valuable insights into combining these powerful tools.
Considered a classic in the field, this book provides a broad overview of simulation methods for social scientists, including a significant focus on agent-based modeling. While an older publication, it offers valuable foundational knowledge and context for the application of ABM in social science research. It's more valuable as additional reading for historical context than a current technical reference.
Focuses on agent-based modeling in social systems, exploring how it can be used to understand complex social phenomena.
This textbook provides an accessible yet technically-oriented introduction to modeling and analysis in complex systems science, with a chapter dedicated to agent-based models. It covers a broad range of modeling techniques and is suitable for students new to complex systems. It includes examples and exercises using Python.
Provides a practical guide to building simulation models using AnyLogic, a software that supports agent-based, system dynamics, and discrete-event modeling. It includes hands-on examples and is suitable for users of AnyLogic interested in multimethod modeling. It's a useful reference for professionals and advanced students using this specific software.
Provides a historical and accessible overview of the origins and development of complexity science, including the work done at the Santa Fe Institute, which has been influential in ABM. It's excellent for gaining broad context and understanding the scientific movement behind ABM.
Reflects on the success of complexity science, including agent-based modeling, by reprinting key articles from different disciplines. It provides an overview of ABM as a modeling approach within the broader context of complex systems. It's useful for gaining a historical and interdisciplinary perspective on ABM.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/6297b0/agent
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