Seeing Agent-based Modelling by Liuhuaying Yang

Imagine a city that loves sushi, where access to sushi is key to happiness. This playful, fictional example serves as the foundation for exploring how individuals make decisions that impact overall happiness. Are they happiest living near sushi houses, or are they willing to travel farther for more options, or perhaps trade sushi with their neighbors? While people initially enjoyed the convenience of traveling for sushi, the long-term result showed that having sushi within their own areas turned out to be the key to happiness.
Agent-based modeling (ABM) is a tool that can help us explore such questions. It simulates the behavior of individual agents—like sushi lovers in this city—and shows how their interactions can create patterns that affect the entire community. ABM allows us to study real-world issues like social inequality, the spread of diseases like COVID-19, and the resilience of systems like supply chains.
This interactive visual tutorial is designed for students, data journalists, and researchers interested in learning how ABM works. Unlike traditional textbooks, this tutorial avoids technical jargon and simplifies complex concepts by leveraging an interactive visual approach that makes it easier to grasp the core principles of ABM, walk through the process of building simulations, and observe the immediate effects of different agent behaviors.