Mathematicians have been working hard to create ways to measure #Gerrymandering. 2017 was a big year on this: accumulating evidence showed PA most gerrymandered of all by two measures, among five worst by any measure. We all lose our voice when our votes don't count. pic.twitter.com/5fgCxTc00r
— LWV Pennsylvania (@LWVPA) February 13, 2018
Today, Philly.com highlights multiple measures of partisan gerrymandering, including several developed here at Princeton.
Now, a major disclaimer: I didn’t think of these, exactly. Several tests (mean-median difference, and lopsided-wins) are over a hundred years old. Another (simulated elections) relies on an equally-old technique, Monte Carlo simulations. These tests are so old that they have whiskers.
There’s one lesson, though: there isn’t just one way to evaluate a gerrymander. Think of these as tools that capture different aspects of partisan asymmetry. For example, Monte Carlo simulations actually account for some of the clustering effect that comes from Republicans gravitating toward rural areas and Democrats gravitating toward population centers.
For more on the tests, see our explainer. If you like, support our work!
On tools for evaluating Gerrymandering, nobody starts from scratch.
Isaac Newton in 1675: “If I have seen further it is by standing on the shoulders of Giants.”
LondonYoung, thank you for this post. Its very inspiring.