Agent-based models (ABMs) are ideal for investigating the emergent properties of complex systems such as insect colony foraging. We use them to understand how the behaviours of individual ants affects the performence of the colony.
Using ABMs allows us to run experiments that are not possible in vivo, by running them in silico. For example, we can change how individual ants make value judgments about food sources, and see how this affects colony efficiency. Would strictly rational ants be more efficient than realistic ants? What if the ants made value judgements like humans? Are different strategies more effective in different enviroments? These are questions that would be impossible to test in real life, but ABMs can give us insights into why animals have evolved to perceive and react to the world as they do.