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Algorithmic Redistricting: Elections made-to-order
About this video
AlphaPhoenix uses Monte Carlo simulation and annealing-style methods to draw North Carolina congressional district maps, both gerrymandered and fair. He focuses on the fascinating computational problem rather than the politics.
Why it appears in the archive
HN recommends it as an entertaining demonstration of how powerful algorithmic gerrymandering can be.
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From the Hacker News discussion
For a great (and entertaining!) look into how you can use simulations to generate these types of maps you should check out this video by the superb channel AlphaPhoenix: [VIDEO]
Read original comment ↗Required watching to put things into perspective of how powerful gerrymandering can be [VIDEO] There was a proposed (and failed) ballot initiative in Ohio that tried to address this in the only way you actually can. The tl;dr is that there is no such thing as a fair map and so the state would be required to gerrymander the districts to match the state-wide election results. Basically proportional representation with extra steps, but brings the "secret metric" in map making to the forefront and requires it be a specific value so you can't tune it to your favor.
Read original comment ↗This aspect of adding randomness to avoid local randomness reminds me of AlphaPhoenix’s automated gerrymandering project: [VIDEO]
Read original comment ↗