Agent-based models are a simulation technique that can be used to study complex systems and the behaviour of agents within them. These models are created by constructing artificial populations and placing them in an artificial environment that resembles the real world. The artificial agents within the similation interact with each other and their environment in a way that mirrors real-life interactions.
Development
Take the study of food security. With an agent-based model, it is possible to create a simulation of a very specific rural population, modify elements to reflect different policy scenarios and observe their impacts.
In order to make the model as accurate as possible, it is important to understand the system that is being modelled. Visiting the system and talking to experts in the field is key to developing agent- based models; including more input into the model improves its ability to predict.
It is also important to implement the results of real-life situations within the model as another source of input. If the model predicts that a certain policy would succeed but it fails when it is implemented in real life, this experience should be incorporated into the model in order for it to learn. Input is key for models to succeed and thus to help us find solutions to the world’s problems.
If you’d like to learn more about this topic, check out this Geoversity course on Introduction to Agent Based Modelling
