选举系统模拟器：一个用于在不同选民分布场景中比较选举机制的开放框架
Electoral Systems Simulator: An Open Framework for Comparing Electoral Mechanisms Across Voter Distribution Scenarios
AI总结：
本文提出一个开源框架，用于在不同选民分布下比较多种选举机制，评估其性能并引入一种新的理论机制作为基准。
摘要：
本文介绍了\texttt{electoral\_sim}，一个开源的Python框架，用于在多样化的选民偏好分布下模拟和比较选举系统。该框架将选民和候选人表示为二维意识形态空间中的点，从欧几里得偏好距离推导出真诚的选票配置，并评估几种标准选举机制——包括多数制、排序选择制、批准制、评分制、康多塞制以及两种比例代表制——以共同的初级指标：选举结果与选民分布几何中位数之间的欧几里得距离。我们评估这些系统在许多基于实证的场景中，从单峰共识选民到极化双峰配置，报告了每个场景下200次试验中的单次运行和蒙特卡洛稳定性结果。作为框架扩展性的案例研究，我们实现了并评估了一个目前任何司法管辖区都不实施的新型假设机制——其中每个选民的影响通过玻尔兹曼softmax核分布到候选人上。该系统作为理论基准，刻画了一个近似上界，而非作为政策提案。所有代码均已公开发布在https://github.com/mukhes3/electoral_sim。
英文摘要：
Here we present \texttt{electoral\_sim}, an open-source Python framework for simulating and comparing electoral systems across diverse voter preference distributions. The framework represents voters and candidates as points in a two-dimensional ideological space, derives sincere ballot profiles from Euclidean preference distances, and evaluates several standard electoral mechanisms -- including plurality, ranked-choice, approval, score, Condorcet, and two proportional representation systems -- against a common primary metric: the Euclidean distance between the electoral outcome and the geometric median of the voter distribution. We evaluate these systems across many empirically-grounded scenarios ranging from unimodal consensus electorates to sharply polarised bimodal configurations, reporting both single-run and Monte Carlo stability results across 200 trials per scenario. As a case study in framework extensibility, we implement and evaluate a novel hypothetical mechanism that is not currently implemented in any jurisdiction -- in which each voter's influence is distributed across candidates via a Boltzmann softmax kernel. This system is included as a theoretical benchmark characterising an approximate upper bound on centroid-seeking performance, rather than as a policy proposal. All code is released publicly at https://github.com/mukhes3/electoral_sim.
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。
