agent-based modeling

I presented a paper titled, "Non-Pharmaceutical Herd Immunity using Homemade Masks" at the 2020 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS) on 20 October 2020.

On 8 October 2020 I presented a paper to the Computational Social Science Society of the Americas annual conference CSS 2020. I am the lead author of the paper, "Creating Intelligent Agents: Combining Agent-Based Modeling with Machine Learning" with my co-author, Dr. Andrew Crooks.

While conducting research for a new agent-based model I came across a 2013 paper titled, "Testing the efficacy of homemade masks: would they protect in an influenza pandemic?" It details how well different materials fared when faced, no pun intended, with the task of stopping a virus.

In my previous blog, I provided a model of COVID-19 based on the U.S. population. Running that model demonstrates how social distancing can flatten the curve by reducing the load on the U.S. health system. Here I provide a few data graphics from an analysis of the model results.

I have created a simulation of the COVID-19 virus (SARS-CoV-2) in the United States population. It demonstrates how social distancing "flattens the curve." The model is written in NetLogo and will run from this website in your browser.

Anyone exposed to Computational Social Science (CSS) and one of it's tools, agent-based modeling, has been presented with a bird flocking model. These "boids" follow three rules as described by Reynolds (1987).

PLOS ONE has a paper titled "Scientific discovery in a model-centric framework: Reproducibility, innovation, and epistemic diversity" in which the authors used agent-based modeling to explore the idea of reproducibility of scientific results. To do so they created a framework of scientists' pursuit of a scientific truth, removed incentives and questionable research practices, and used model comparison rather than statistical hypothesis

The hardware on which your simulation runs can change your approach to the problem and the skills you need. At the very least, using modern hardware should make your experiments go faster. During my master's program, I used an ASUS laptop for most of my modeling efforts. It was fine for most simulations, particularly when using NetLogo, but was inadequate for scaling to larger models. The 8 logical cores did, however, allow me to take advantage of NetLogo's Behavior Space to implement a type of parallelism, Single Program, Multiple Data (SPMD).

A well-used aphorism from British Mathematician Dr. George Box is, "All models are wrong. Some are useful." (George E. P. Box) The reason they are wrong is that models are a simplified representation of some real system, often a complex one. Repeatability is therefore an important component in discovering a model's validity. Yet there is some evidence that there is a crisis in the world of science in that many results cannot be reproduced.

NetLogo is a modeling framework used for agent-based, individual based, and cellular automata modeling. It is the most common software used for modeling at George Mason University's Computational Social Science (CSS) program. The course, CSS 600: Introduction to Computational Social Science, assists a new modeler in using this tool. NetLogo provides a Graphic User Interface (GUI) designer and a programming language that has its roots in Lisp via Logo.