Agent based models in Mata: Modeling aggregate processes

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, the adoption of new technologies, or the spread of a disease. In this talk, I will discuss the abm package. This package contains a number of Mata classes intended to help manage various aspects of an ABM. Implementing a new ABM will always require that person developing the ABM does some programming, but many tasks will be similar across ABMs. For example, in many ABMs, the agents live on a square grid (like a chessboard), and can only interact with their neighbors. The abm_grid class, which is part of the abm package, contains a set of Mata functions that will do those tasks. Alternatively, the agents could live in a network, and the abm_nw class is there to handle that case. The talk will begin with a brief introduction to Mata and classes in Mata and than discuss how to use the classes in the abm package to create a ABM by going through various examples.