Multiobjective Optimization for Politically Fair Districting: A Scalable Multilevel Approach
提出一种多目标优化模型，将政治公平指标（如效率差距、党派对称性）纳入选区划分，并设计可扩展的多层算法，以威斯康星州国会选区为例验证了平衡选民与政党利益的方案。
Gerrymandering has been a fundamental issue in American democracy for more than two centuries, with significant implications for electoral representation. Traditional optimization models for political districting primarily model nonpolitical fairness metrics such as the compactness of districts. In “Multiobjective Optimization for Politically Fair Districting: A Scalable Multilevel Approach,” Swamy, King, and Jacobson propose optimization models that explicitly incorporate political fairness objectives using political data from past elections. These objectives model fundamental fairness principles such as vote-seat proportionality (efficiency gap), partisan (a)symmetry, and competitiveness. They propose a solution strategy, called the multilevel algorithm, that solves large instances of the problem using a series of matching-based graph contractions. A case study on congressional districting in Wisconsin demonstrates that district plans balance the interests of the voters and the political parties.