Gerrymandering is the (currently very relevant) issue in representative democracies of drawing electoral districts to give a political advantage to one party or group. Political events within the last few years have sparked a lot of research activity from mathematicians, computer scientists, and statisticians related to detecting and quantifying gerrymandered electoral plans. In this talk I will give an introduction to the problem of gerrymandering, discuss some historical attempted methods of quantifying gerrymanders and their shortcomings, and then talk about a promising new method called ``metagraph Markov Chain Monte Carlo'' currently being researched and implemented.