Parameter Effects in ReCom Ensembles

TL;DR

This study systematically examines how different parameters in ReCom ensemble methods influence redistricting outcomes, highlighting the importance of careful parameter selection for reliable analysis.

Contribution

It introduces new methods to assess convergence and redundancy in ReCom ensembles, providing insights into parameter effects across multiple states and legislative chambers.

Findings

Population tolerance has minimal impact on scores.

Algorithm and county-preservation parameters significantly affect metrics.

Parameter effects vary across jurisdictions, emphasizing careful choice.

Abstract

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.