A visual explainer for understanding agent-based modelling
Agent-based modelling (ABM) has evolved into a powerful tool for exploring social dynamics across various time scales. 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.
Agent-based modeling has become widely popular in research across various fields, with a noticeable increase in publications. Researchers are increasingly using this computational approach to simulate individual agents' behaviors in complex systems, contributing to a growing trend in scientific literature. As exemplified by the rising number of publications related to agent-based modeling in PubMed (retrieved on March 5, 2024), it reflects its effectiveness in studying complex biological, medical, and public health phenomena.
This project was created by Liuhuaying Yang from Complexity Science Hub, in collaboration with Dr. Iza Romanowska, the author of Agent-Based Modeling for Archaeology.
The beginner model we present is inspired by Schelling's segregation model but with modifications to better introduce the model concepts.
Interested in this segregation model? You can check Nicky Case's Parable of the Polygons.
Interested in other models? Check Dirk Brockmann's Complexity Explorables and NetLogo Models Library.