A Uniformly Random Solution to Algorithmic Redistricting
<|reference_start|>A Uniformly Random Solution to Algorithmic Redistricting: The process of drawing electoral district boundaries is known as political redistricting. Within this context, gerrymandering is the practice of drawing these boundaries such that they unfairly favor a particular political party, often leading to unequal representation and skewed electoral outcomes. One of the few ways to detect gerrymandering is by algorithmically sampling redistricting plans. Previous methods mainly focus on sampling from some neighborhood of ``realistic' districting plans, rather than a uniform sample of the entire space. We present a deterministic subexponential time algorithm to uniformly sample from the space of all possible $ k $-partitions of a bounded degree planar graph, and with this construct a sample of the entire space of redistricting plans. We also give a way to restrict this sample space to plans that match certain compactness and population constraints at the cost of added complexity. The algorithm runs in $ 2^{O(\sqrt{n}\log n)} $ time, although we only give a heuristic implementation. Our method generalizes an algorithm to count self-avoiding walks on a square to count paths that split general planar graphs into $ k $ regions, and uses this to sample from the space of all $ k $-partitions of a planar graph.<|reference_end|>
@article{cai2024a,
    title={A Uniformly Random Solution to Algorithmic Redistricting},
    author={Jin-Yi Cai, Jacob Kruse, Kenneth Mayer, Daniel P. Szabo},
    journal={arXiv preprint arXiv:2402.13868},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13868},
    primaryClass={cs.DS cs.DM math.CO}
}