Mathematics, Politics, and Justice Denied

In the 2020 elections for the North Carolina State House, Democrats received 49 percent of the votes but won only 42.5 percent of the seats. In three-quarters of the state-level elections, the winning margin was more than 20 percentage points—in other words, landslides—even though statewide, the margins between the two main political parties is razor-thin—at the presidential level, Trump beat Biden by less than 2 percent, and a Democrat won the 2020 governor’s race. That’s gerrymandering, the process by which a state is divided up in such a way as to maximize the number of electoral seats one particular party is likely to win.

There are two ways to gerrymander. In one, you concentrate your opposition’s likely voters into a single district, giving that one away but winning all or most of the surrounding areas. In the other, you divide a concentration of likely voters into two or more districts in such a way that they’ll fall short of a majority.

Gerrymandering is obviously unfair, but creating fair districts is harder than it looks. So political operatives and consultants draw up various maps, maximizing this or that, but mostly their party’s interests.

If this seems instead like a job for computer-aided statistical analysis, it is. Several years ago, researchers in North Carolina got the idea of generating thousands—even tens of thousands—of maps, and creating algorithms that maximize the desired variables to the extent possible.

Jonathan Mattingly is a Professor of Statistical Science, and a Professor of Mathematics at Duke University He leads a group at Duke that conducts non-partisan research to understand and quantify gerrymandering.

A @RadioSpectrum1 conversation with Duke University Professor of Mathematics Jonathan Mattingly. Available on Spotify, Apple, and @IEEESpectrum.