Configuration in Finite-Sample Redistricting Ensemble Inference: A Texas Case Study
Simulation-based redistricting analysis evaluates an enacted redistricting plan by comparing it with simulated alternatives. Even when a rank test has finite-sample validity under a fixed simulation configuration, the conclusions may still depend on how the simulation is configured. We study this phenomenon using Texas’ enacted congressional redistricting plan (PLANC2333) as a case study. Our workflow combines a 9,712-precinct spatial graph, reversible recombination, and Besag-Clifford finite-sample rank inference. We vary the following configuration settings: trajectory length, independently generated hub realizations, geographic constraints, and the statistic used to define minority electoral opportunity. We find that variation in these configuration settings can yield different plan comparisons. These results demonstrate our simple central message: finite-sample validity does not imply configuration robustness. We therefore recommend that simulation-based redistricting analyses report the simulation configurations used and assess whether their conclusions are robust across reasonable configuration choices.