The Essential Role of Empirical Validation in Legislative Redistricting Simulation

TL;DR

This paper emphasizes the importance of empirical validation for redistricting simulation methods, demonstrating a scalable enumeration algorithm and assessing existing methods' accuracy using realistic data.

Contribution

It introduces an efficient enumeration algorithm for all possible redistricting plans and evaluates the accuracy of current simulation methods through empirical validation.

Findings

The enumeration algorithm scales to states with hundreds of units.

Existing simulation methods have varying degrees of accuracy.

Empirical validation is crucial for trustworthy redistricting simulations.

Abstract

As granular data about elections and voters become available, redistricting simulation methods are playing an increasingly important role when legislatures adopt redistricting plans and courts determine their legality. These simulation methods are designed to yield a representative sample of all redistricting plans that satisfy statutory guidelines and requirements such as contiguity, population parity, and compactness. A proposed redistricting plan can be considered gerrymandered if it constitutes an outlier relative to this sample according to partisan fairness metrics. Despite their growing use, an insufficient effort has been made to empirically validate the accuracy of the simulation methods. We apply a recently developed computational method that can efficiently enumerate all possible redistricting plans and yield an independent uniform sample from this population. We show that this algorithm scales to a state with a couple of hundred geographical units. Finally, we empirically examine how existing simulation methods perform on realistic validation data sets.