Simulated redistricting plans for the analysis and evaluation of redistricting in the United States

• URL: http://arxiv.org/abs/2206.10763v2
• Date: Fri, 21 Oct 2022 01:48:58 GMT
• Title: Simulated redistricting plans for the analysis and evaluation of redistricting in the United States
• Authors: Cory McCartan, Christopher T. Kenny, Tyler Simko, George Garcia III, Kevin Wang, Melissa Wu, Shiro Kuriwaki, and Kosuke Imai
• Abstract summary: This article introduces the 50stateSimulations, a collection of simulated congressional districting plans and underlying code. The 50stateSimulations allow for the evaluation of enacted and other congressional redistricting plans in the United States.
• Score: 0.2529563359433233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This article introduces the 50stateSimulations, a collection of simulated congressional districting plans and underlying code developed by the Algorithm-Assisted Redistricting Methodology (ALARM) Project. The 50stateSimulations allow for the evaluation of enacted and other congressional redistricting plans in the United States. While the use of redistricting simulation algorithms has become standard in academic research and court cases, any simulation analysis requires non-trivial efforts to combine multiple data sets, identify state-specific redistricting criteria, implement complex simulation algorithms, and summarize and visualize simulation outputs. We have developed a complete workflow that facilitates this entire process of simulation-based redistricting analysis for the congressional districts of all 50 states. The resulting 50stateSimulations include ensembles of simulated 2020 congressional redistricting plans and necessary replication data. We also provide the underlying code, which serves as a template for customized analyses. All data and code are free and publicly available. This article details the design, creation, and validation of the data.

Related papers

• NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking [65.24988062003096]
  We present NAVSIM, a framework for benchmarking vision-based driving policies. Our simulation is non-reactive, i.e., the evaluated policy and environment do not influence each other. NAVSIM enabled a new competition held at CVPR 2024, where 143 teams submitted 463 entries, resulting in several new insights.
  arXiv  Detail & Related papers  (2024-06-21T17:59:02Z)
• PDDLEGO: Iterative Planning in Textual Environments [56.12148805913657]
  Planning in textual environments has been shown to be a long-standing challenge even for current models. We propose PDDLEGO that iteratively construct a planning representation that can lead to a partial plan for a given sub-goal. We show that plans produced by few-shot PDDLEGO are 43% more efficient than generating plans end-to-end on the Coin Collector simulation.
  arXiv  Detail & Related papers  (2024-05-30T08:01:20Z)
• Near-optimal Policy Identification in Active Reinforcement Learning [84.27592560211909]
  AE-LSVI is a novel variant of the kernelized least-squares value RL (LSVI) algorithm that combines optimism with pessimism for active exploration. We show that AE-LSVI outperforms other algorithms in a variety of environments when robustness to the initial state is required.
  arXiv  Detail & Related papers  (2022-12-19T14:46:57Z)
• Mathematically Quantifying Non-responsiveness of the 2021 Georgia Congressional Districting Plan [3.097163558730473]
  We use a Metropolized-sampling technique through a parallel tempering method combined with ReCom. We develop these improvements through the first case study of district plans in Georgia. Our analysis projects that any election in Georgia will reliably elect 9 Republicans and 5 Democrats under the enacted plan.
  arXiv  Detail & Related papers  (2022-03-13T02:58:32Z)
• Measuring Geometric Similarity Across Possible Plans for Automated Redistricting [0.0]
  This paper briefly introduces an interpretive measure of similarity, and a corresponding assignment matrix, that corresponds to the percentage of a state's area or population that stays in the same congressional district between two plans. We then show how to calculate this measure in an intuitive time and briefly demonstrate some potential use-cases.
  arXiv  Detail & Related papers  (2021-11-17T03:37:25Z)
• BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym [59.53949960353792]
  BayesSimIG is a library that provides an implementation of BayesSim integrated with the recently released NVIDIA IsaacGym. BayesSimIG provides an integration with NVIDIABoard to easily visualize slices of high-dimensional posteriors.
  arXiv  Detail & Related papers  (2021-07-09T16:21:31Z)
• Reinforcement Learning for Adaptive Mesh Refinement [63.7867809197671]
  We propose a novel formulation of AMR as a Markov decision process and apply deep reinforcement learning to train refinement policies directly from simulation. The model sizes of these policy architectures are independent of the mesh size and hence scale to arbitrarily large and complex simulations.
  arXiv  Detail & Related papers  (2021-03-01T22:55:48Z)
• A User's Guide to Calibrating Robotics Simulators [54.85241102329546]
  This paper proposes a set of benchmarks and a framework for the study of various algorithms aimed to transfer models and policies learnt in simulation to the real world. We conduct experiments on a wide range of well known simulated environments to characterize and offer insights into the performance of different algorithms. Our analysis can be useful for practitioners working in this area and can help make informed choices about the behavior and main properties of sim-to-real algorithms.
  arXiv  Detail & Related papers  (2020-11-17T22:24:26Z)
• Colorado in Context: Congressional Redistricting and Competing Fairness Criteria in Colorado [0.0]
  We generate a large random sample of reasonable redistricting plans and determine the partisan balance of each district using returns from state-wide elections in 2018. We investigate the relationships between partisan outcomes, number of counties which are split, and number of competitive districts in a plan.
  arXiv  Detail & Related papers  (2020-11-11T20:05:50Z)
• 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)
• The Essential Role of Empirical Validation in Legislative Redistricting Simulation [0.0]
  We apply a recently developed computational method that can efficiently enumerate all possible redistricting plans. We show that this algorithm scales to a state with a couple of hundred geographical units.
  arXiv  Detail & Related papers  (2020-06-17T20:51:43Z)

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.