By definition, Agent-Based Models (ABM) are small-scale computational models used to simulate the actions and interactions of agents, which may represent individuals or groups.

Imagine you are a graduate researcher studying Drosophila as your model organism. To support their survival, you need to prepare a culture medium and test the effects of different variables—say, for example, how different concentrations of perfumes impact Drosophila growth. However, you’re unsure of the exact outcomes and don’t have the time to conduct multiple rounds of experiments. You want to achieve optimal results on your first attempt.

This is where ABM can be useful. In this case, the Drosophila would be the agents in your model, and the culture medium would be the environment you’re trying to simulate. You would build a computational model incorporating the factors you want to test, such as the components of the growth medium, the concentration of perfume, the initial number of Drosophila you plan to start with, and so on. By running simulations in the ABM, you can explore different scenarios and predict outcomes, saving time and resources.

Attaching an image of my drosophila culture from 2017 where I had several culture bottles wasted because I couldn’t measure the components of the culture medium and ended up having several failed experiments.

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