Agent-Based Modeling

Imagine a small town that has been struck by a contagious disease. Each person in the town could be healthy, sick, or recovered, and their state changes depending on who they come into contact with. If a healthy person spends time near someone who is sick, they might catch the illness. After a period of being sick, people recover and no longer spread the disease. As individuals go about their daily business, moving through shops, schools, and public spaces, the disease spreads or dies out depending on these everyday interactions.

This kind of situation—where the outcome for the whole community depends on the simple, local decisions of many individuals—is exactly the sort of system that agent-based modeling is designed to explore. Instead of treating the town as a single unit, agent-based models consider each person (or agent) separately, giving them their own rules and behaviors. When all these agents interact, larger patterns begin to emerge, like waves of infection or the eventual decline of the outbreak. 

Agent-based modeling (ABM) focuses on understanding complex systems by simulating individual agents and their interactions within an environment.¹ This approach helps researchers make sense of these bottom-up processes, where no single individual is directing the system, but collective patterns still arise. By recreating scenarios like a town facing an epidemic, it allows researchers to test “what if” questions—like what would happen if people social distance, or if a vaccine is introduced—and see how those choices ripple outward to shape the bigger picture.

ABM is a type of microscale model, a computer simulation that looks closely at the small details of a system. It’s the opposite of macroscale models, which simplify things by grouping people or actions into larger categories. When exploring a problem, researchers can combine microscale and macroscale approaches to see both the fine-grained interactions that drive change and the bigger patterns that emerge at the system level. 

The application of ABM goes far beyond public health and epidemics. In fact, agents can be pretty much whatever you want them to be, such as ants in a colony, consumers in an economy, particles in a gas, or even galaxies in the universe. Wherever there’s a large and complex system to understand, ABM can be applied. This approach has been utilized across various fields: in economics to explore how small investor choices can ripple through markets, in urban planning to understand how traffic jams form even without accidents, in sociology to analyze how misinformation spreads through social networks, and in geography to see how communities recover—or struggle to recover—after natural disasters.