“What we propose is to free redistricting from party political biases,” she said. Their algorithm was created using an open-source code that’s available for anyone to reproduce the results, which Guest believes is crucial to the transparency of the model. “The use of open-source software and transparent, easy to understand code, would help keep the process unbiased, and allow people to verify and trust the results.”
The model they tested was based on the simplest possible conditions: voters within the same district should be geographically close. They believe politicians shouldn’t be involved in district-mapping at all, relying instead on computers to do the work.
“Districting should be no different than multiplying two large numbers together using a calculator,” Guest remarked. “One knows the numbers, but relies on the computer to do the calculation properly.”
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The researchers were inspired to devise a solution to partisan gerrymandering because they see it as a threat to democracy.
“Gerrymandering is corrosive to basic democratic values,” Bradley Love, another experimental psychology researcher at University College London and a co-creator of the algorithm, told Seeker.
“In extreme cases, it disenfranchises citizens by creating districts in which results are virtually preordained,” he said. “Government can become less representative and responsive to the will of the people, and instead [become] captured by special and powerful interests.”
In developing the formula, the research team aimed to test the difference in population concentration between computed districts and districts that currently exist. They theorized that the density would be largest for big states, which would be challenging for any human to fairly and accurately separate into districts. To evaluate this, they created a clustering algorithm to redraw lines for all 435 congressional districts in the US, adhering to a federal law that requires all districts to have roughly the same population size.
“The model starts with one cluster for each district at random locations within the state,” Guest explained. “At the start of each round, the model assigns people to the nearest cluster. At the end of the round, each cluster updates its location to be in the center of its people. Then, using these updated positions, a new round begins with everyone reassigned to the nearest cluster once again. This goes on and on until the clusters stabilize, defining the voting districts. The one nuance is that clusters with more people are penalized so that each cluster ends up with roughly the same number of people.”
One aspect of redistricting that many states require is the preservation of communities of interest — people who are demographically similar based on race, class, and/or culture. Because these groups are likely to have similar political concerns, they benefit from having unified representation in the legislature. But grouping voters based on geography could split these communities apart, which is why geography alone is not sufficient criteria for redistricting.