Intro Blog: A Standard Schellings Model

Hi, I’m Adithya Prabha and I’m currently working in algorithms at a lab in George Mason University (GMU). I’m working with Schelling’s segregation model in an attempt to make the model more “unified”.

What exactly does that mean? We’re redefining “Schelling models” as Markov chains driven by Metropolis-Hastings algorithms. Think of it kinda like this: instead of messy, agent-based simulations that freeze up, we parameterize everything (graph, demographics, proposal distributions, etc). This lets us ensure that the model is ergodic (meaning it settles into a unique long-term state regardless of where you start). I’ve also started working on a practioner’s cookbook with “recipes” for constructing other famous Schelling-like models.

Why do this? Schelling’s 1971 work showed how a large amount of small scale changes can result in a very segregated city (large-scale change). Today’s variants of the same model in different fields are all over the place. They are super relevant in social sciences, physics, and even math. Unfortunately, these variants sometimes get stuck in “absorbing states” or even oscillate wildly. We hope to add stochastic rigor so that our simulations are reliable and most importantly, comparable.

From my own point of view, I hope to better understand theoretical computer science, write a paper, and become a more articulate presenter. On a wider level, I hope we successfully complete this project because it would enable more accurate modeling across a variety of fields.