Compactness statistics for spanning tree recombination
- URL: http://arxiv.org/abs/2103.02699v2
- Date: Tue, 18 May 2021 01:39:59 GMT
- Title: Compactness statistics for spanning tree recombination
- Authors: Jeanne N. Clelland, Nicholas Bossenbroek, Thomas Heckmaster, Adam
Nelson, Peter Rock, Jade VanAusdall
- Abstract summary: "ReCom" method produces plans with more compact districts than some other methods.
We construct ensembles of 2-district plans for two grid graphs and for the precinct graph of Boulder County, CO.
This is an important step towards understanding compactness properties for districting plans produced by the ReCom method.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Ensemble analysis has become an important tool for quantifying
gerrymandering; the main idea is to generate a large, random sample of
districting plans (an "ensemble") to which any proposed plan may be compared.
If a proposed plan is an extreme outlier compared to the ensemble with regard
to various redistricting criteria, this may indicate that the plan was
deliberately engineered to produce a specific outcome.
Many methods have been used to construct ensembles, and a fundamental
question that arises is: Given a method for constructing plans, can we identify
a probability distribution on the space of plans that describes the probability
of constructing any particular plan by that method?
Recently, MCMC methods have become a predominant tool for constructing
ensembles. Here we focus on the MCMC method known as "ReCom," which was
introduced in 2018 by the MGGG Redistricting Lab. ReCom tends to produce plans
with more compact districts than some other methods, and we sought to better
understand this phenomenon. We adopted a discrete analog of district perimeter
called "cut edges" as a quantitative measure for district compactness; this
measure was proposed by Duchin and Tenner, and it avoids some of the
difficulties associated with compactness measures based on geographic
perimeter, such as the Polsby-Popper score.
To model the basic ReCom step, we constructed ensembles of 2-district plans
for two grid graphs and for the precinct graph of Boulder County, CO. We found
that the probability of sampling any particular plan -- which is roughly
proportional to the product of the numbers of spanning trees for each of the
two districts -- is also approximately proportional to an exponentially
decaying function of the number of cut edges in the plan. This is an important
step towards understanding compactness properties for districting plans
produced by the ReCom method.
Related papers
- The Traveling Mailman: Topological Optimization Methods for User-Centric Redistricting [0.0]
This study introduces a new districting approach using the US Postal Service network to measure community connectivity.
We combine Topological Data Analysis with Markov Chain Monte Carlo methods to assess district boundaries' impact on community integrity.
arXiv Detail & Related papers (2024-07-28T16:50:45Z) - Multiscale Parallel Tempering for Fast Sampling on Redistricting Plans [1.1233768932957773]
A persuasive method is to compare the plan with an ensemble of neutrally drawn redistricting plans.
To audit the partisan difference between the ensemble and a given plan, one must ensure that the non-partisan criteria are matched.
In this work, we generate a multiscale parallel tempering approach that makes local moves at each scale.
arXiv Detail & Related papers (2024-01-30T21:33:05Z) - Planning as In-Painting: A Diffusion-Based Embodied Task Planning
Framework for Environments under Uncertainty [56.30846158280031]
Task planning for embodied AI has been one of the most challenging problems.
We propose a task-agnostic method named 'planning as in-painting'
The proposed framework achieves promising performances in various embodied AI tasks.
arXiv Detail & Related papers (2023-12-02T10:07:17Z) - Spanning tree methods for sampling graph partitions [0.7658085223797904]
A districting plan can be viewed as a balanced partition of a graph into connected subsets.
RevReCom converges to the simple, natural distribution that ReCom was originally designed to approximate.
arXiv Detail & Related papers (2022-10-04T06:18:33Z) - Compact Redistricting Plans Have Many Spanning Trees [39.779544988993294]
In the design and analysis of political redistricting maps, it is often useful to be able to sample from the space of all partitions of the graph of census blocks into connected subgraphs of equal population.
In this paper, we establish an inverse exponential relationship between the total length of the boundaries separating districts and the probability that such a map will be sampled.
arXiv Detail & Related papers (2021-09-27T23:36:01Z) - Exact Recovery in the General Hypergraph Stochastic Block Model [92.28929858529679]
This paper investigates fundamental limits of exact recovery in the general d-uniform hypergraph block model (d-HSBM)
We show that there exists a sharp threshold such that exact recovery is achievable above the threshold and impossible below it.
arXiv Detail & Related papers (2021-05-11T03:39:08Z) - Sequential Monte Carlo for Sampling Balanced and Compact Redistricting
Plans [0.0]
We present a new Sequential Monte Carlo (SMC) algorithm that generates a sample of redistricting plans converging to a realistic target distribution.
We validate the accuracy of the proposed algorithm by using a small map where all redistricting plans can be enumerated.
We then apply the SMC algorithm to evaluate the partisan implications of several maps submitted by relevant parties in a recent high-profile redistricting case in the state of Pennsylvania.
arXiv Detail & Related papers (2020-08-13T23:26:34Z) - Planning in Markov Decision Processes with Gap-Dependent Sample
Complexity [48.98199700043158]
We propose MDP-GapE, a new trajectory-based Monte-Carlo Tree Search algorithm for planning in a Markov Decision Process.
We prove an upper bound on the number of calls to the generative models needed for MDP-GapE to identify a near-optimal action with high probability.
arXiv Detail & Related papers (2020-06-10T15:05:51Z) - Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning [78.65083326918351]
We consider alternatives to an implicit sequential planning assumption.
We propose Divide-and-Conquer Monte Carlo Tree Search (DC-MCTS) for approximating the optimal plan.
We show that this algorithmic flexibility over planning order leads to improved results in navigation tasks in grid-worlds.
arXiv Detail & Related papers (2020-04-23T18:08:58Z) - Decentralized MCTS via Learned Teammate Models [89.24858306636816]
We present a trainable online decentralized planning algorithm based on decentralized Monte Carlo Tree Search.
We show that deep learning and convolutional neural networks can be employed to produce accurate policy approximators.
arXiv Detail & Related papers (2020-03-19T13:10:20Z) - Augmented Parallel-Pyramid Net for Attention Guided Pose-Estimation [90.28365183660438]
This paper proposes an augmented parallel-pyramid net with attention partial module and differentiable auto-data augmentation.
We define a new pose search space where the sequences of data augmentations are formulated as a trainable and operational CNN component.
Notably, our method achieves the top-1 accuracy on the challenging COCO keypoint benchmark and the state-of-the-art results on the MPII datasets.
arXiv Detail & Related papers (2020-03-17T03:52:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.