Agentmandering：一种通过大语言模型智能体实现公平选区划分的博弈论框架
Agentmandering: A Game-Theoretic Framework for Fair Redistricting via Large Language Model Agents
AI总结：
本文提出Agentmandering框架，将选区划分建模为两个对立政治利益LLM智能体基于Choose-and-Freeze协议的回合制谈判，实验表明其显著降低党派偏见与方差，提升公平性和稳定性。
摘要：
选区划分在决定选票如何转化为政治权力方面发挥着核心作用。虽然现有的计算方法主要旨在生成大量法律上有效的选区划分方案集合，但它们往往忽视了选择过程中所涉及的战略动态。这一疏忽为党派行为者提供了挑选地图的机会，这些地图虽然在技术上合规，但在政治上有利。当选择过程本身可以被操纵时，仅仅满足形式上的约束并不能确保公平。我们提出Agentmandering框架，将选区划分重新构想为两个代表对立政治利益的智能体之间基于回合的谈判。借鉴博弈论思想，特别是Choose-and-Freeze协议，我们的方法通过大语言模型（LLM）智能体将战略交互嵌入到选区划分过程中。智能体从一小组候选地图中交替选择和冻结选区，通过受约束且可解释的选择逐步划分州。基于2020年后美国人口普查数据在所有州的评估表明，Agentmandering显著降低了党派偏见和不公平性，同时实现了比标准基线低2到3个数量级的方差。这些结果证明了公平性和稳定性，尤其是在摇摆州场景中。我们的代码可在https://github.com/Lihaogx/AgentMandering获取。
英文摘要：
Redistricting plays a central role in shaping how votes are translated into political power. While existing computational methods primarily aim to generate large ensembles of legally valid districting plans, they often neglect the strategic dynamics involved in the selection process. This oversight creates opportunities for partisan actors to cherry-pick maps that, while technically compliant, are politically advantageous. Simply satisfying formal constraints does not ensure fairness when the selection process itself can be manipulated. We propose \textbf{Agentmandering}, a framework that reimagines redistricting as a turn-based negotiation between two agents representing opposing political interests. Drawing inspiration from game-theoretic ideas, particularly the \textit{Choose-and-Freeze} protocol, our method embeds strategic interaction into the redistricting process via large language model (LLM) agents. Agents alternate between selecting and freezing districts from a small set of candidate maps, gradually partitioning the state through constrained and interpretable choices. Evaluation on post-2020 U.S. Census data across all states shows that Agentmandering significantly reduces partisan bias and unfairness, while achieving 2 to 3 orders of magnitude lower variance than standard baselines. These results demonstrate both fairness and stability, especially in swing-state scenarios. Our code is available at https://github.com/Lihaogx/AgentMandering.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
