A visual explainer for understanding agent-based modelling.



A visual explainer for Agent-Based Modelling

Do you ever wonder how cities become segregated when everyone prefers to live among people of the same type, such as white people preferring white neighborhoods? Or how the Covid-19 pandemic might have unfolded without quarantine policies? Agent-based modelling (ABM) can help us examine these social issues and more by modelling individual agents’ interactions and actions.

ABM has been used to study a wide range of topics, from economics to biology to history to business and sports. This computational approach simulates individual agents’ behavior in complex systems, providing valuable insights into various phenomena.

This visual explainer, created by Liuhuaying Yang, a data visualization expert at the Complexity Science Hub, in collaboration with Iza Romanowska from Aarhus University, makes ABM fun and interactive. It starts with a sushi-lover city where citizens’ happiness is determined by how easily they can get sushi. An avatar named Iza, who loves sushi, guides users through the fundamentals of ABM. Sounds fun, right? Through this simple story, users can learn how individual choices influence the overall dynamics and structure of the city.


“In ABM we create virtual agents representing people, animals, or objects. These agents have their own characteristics, make decisions, and interact with each other. By simulating their actions and interactions, we can observe how their individual behaviors give rise to collective patterns and outcomes, offering insights into complex systems and the impact of individual actions on the overall system,” says Yang.

“Our explainer makes ABM easy to understand. Whether you’re a researcher, student, data journalist, analyst, or simply curious, dive into this tool. Discover how individual actions shape our society. From human behavior to economic trends, ABM helps us explore complex systems,” adds Yang. 

Who is using ABM at CSH

Examples of ABM papers from various fields can also be found on the visual explainer website. Here are a few examples: 



A visual explainer for understanding agent-based modelling.

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