An Agent Based Model (ABM) is a simulation in which agents that each follow simple rules interact with one another and thus produce an often surprising outcome at the macro level. The purpose of an ABM is to explore mechanisms through which actions of the individual agents add up to a macro outcome by varying the rules that agents have to follow or varying with whom the agent can interact (for example, varying the network).
These models have many applications, like the study of segregation of neighborhoods or the adoption of new technologies. However, the application that is currently most topical is the spread of a disease. In this talk, I will give introduction on how to implement an ABM in Mata, by going through the simple models I (a sociologist, not an epidemiologist) used to make sense of what is happening with the COVID-19 pandemic.
In this talk I will be discussing the following tools that can help with making an ABM in Mata: