Comparing four modeling approaches: System Dynamics, Agent-based Modeling, Cellular Automata, and Discrete Event Simulation using a SIR model as an example
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Over the years several modeling styles have been developed but often it is unclear what are the differenced between them. In this post, Dr. Crooks and I would like to compare and contrast four modeling approaches widely used in Computational Social Science, namely: System Dynamics (SD) models, Agent-based Models (ABM), Cellular Automata (CA) models, and Discrete Event Simulation (DES). To compare and contrast the differences in how these models work and how their underlying mechanisms generate outputs, we needed a common problem to test them against with the same set of model parameters. While one could choose a more complex example, here we decided to choose one of the simplest models we know. Specifically, we chose to model the spread of a disease specifically using a Susceptible-Infected-Recovered (SIR) epidemic model. Our inspiration for this came from the SD model outlined in the great book “Introduction to Computational Science: Modeling and Simulation for the Sciences” by Shiflet and Shiflet (2014) which was implemented in NetLogo from the accompanying website. For the remaining models (i.e. ABM, CA, and DES) we created models from scratch in NetLogo. Below we will introduce how we built each model, before showing the results from the four models with the same set of parameters, which allows us to compare the results of the models.

A post on the comparison among these models can be found on