A Modular Computational Framework for Section 2 Voting Rights Act Analysis
DOI:
https://doi.org/10.18409/x2fzgx27Keywords:
Voting Rights Act, Recombination Markov Chains, Ecological Inference, Besag-Clifford Exact Inference, Gerrymandering, Electoral Systems, Markov Chain Monte Carlo, Minority Opportunity DistrictsAbstract
This paper introduces a modular computational framework for Section 2 Voting Rights Act analysis integrating redistricting data engineering, Bayesian ecological inference, and ensemble-based exact inference into a unified pipeline. The framework propagates posterior uncertainty from the ecological inference stage through plan-level minority opportunity scoring and evaluates enacted district plans against legally constrained alternative plans generated via recombination Markov chains. To avoid unverifiable convergence assumptions associated with long-run Markov chain Monte Carlo sampling, the framework adopts the Besag-Clifford parallel construction, yielding exact finite-sample p-values under kernel reversibility without requiring convergence to stationarity. A probabilistic minority opportunity functional is introduced and characterized axiomatically as the unique district-additive expected opportunity score satisfying a collection of structural assumptions. A case study on Texas congressional districts demonstrates that the enacted plan systematically underperforms neutral alternatives with respect to minority electoral opportunity.
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