University of Arizona
Browse

Data file artifacts produced for "Parameter Effects in ReCom Ensembles"

dataset
posted on 2025-10-08, 18:05 authored by Todd A ProebstingTodd A Proebsting
Ensemble analysis has become central to redistricting litigation, but parameter effects remain understudied. We analyze 315 ReCom ensembles across the three legislative chambers in 7 states, systematically varying the population tolerance, county preservation strength, and algorithm variant. To validate convergence, we introduce new methods to approximate effective sample size and measure redundancy. We find that varying the population tolerance has a negligible effect on all scores, whereas the algorithm and county-preservation parameters can significantly affect some metrics, inconsistently in some cases but surprisingly consistently in others across jurisdictions. These findings suggest parameter choices should be thoughtfully considered when using ReCom ensembles.<br><br><br><br><hr><i>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</i><p></p>

History

Related Materials

  1. 1.
    arXiv - Is supplement to Parameter Effects in ReCom Ensembles
  2. 2.
    URL - Is supplemented by Knobs
  3. 3.
    URL - Is supplement to Restricted Analytics in Python

Usage metrics

    Science & Mathematics

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC