Research on the causes of political polarization points towards multiple drivers of the problem, from social and psychological to economic and technological. However, political institutions stand out, because -- while capable of exacerbating or alleviating polarization -- they can be re-engineered more readily than others. Accordingly, we analyze one class of such institutions -- electoral systems -- investigating whether the large-party seat bias found in many common systems (particularly plurality and Jefferson-D'Hondt) exacerbates polarization. Cross-national empirical data being relatively sparse and heavily confounded, we use computational methods: an agent-based Monte Carlo simulation. We model voter behavior over multiple electoral cycles, building upon the classic spatial model, but incorporating other known voter behavior patterns, such as the bandwagon effect, strategic voting, preference updating, retrospective voting, and the thermostatic effect. We confirm our hypothesis that electoral systems with a stronger large-party bias exhibit significantly higher polarization, as measured by the Mehlhaff index.

翻译：关于政治极化成因的研究指出，该问题存在多种驱动因素，涵盖社会心理、经济与技术等多个维度。然而，政治制度在其中尤为突出，因为尽管它们可能加剧或缓解极化，但相较于其他因素更易于通过制度再设计进行调整。基于此，我们分析了一类特定的政治制度——选举制度，旨在探究许多常见选举制度（特别是多数制与杰斐逊-迪翁特法）中存在的大党席位偏差是否会加剧政治极化。鉴于跨国实证数据相对稀缺且混杂因素众多，我们采用基于智能体的蒙特卡洛模拟这一计算方法进行研究。我们在经典空间模型的基础上，构建了多轮选举周期中的选民行为模型，同时纳入了其他已知的选民行为模式，如从众效应、策略性投票、偏好更新、回溯性投票及恒温效应。通过Mehlhaff指数的测量，我们证实了研究假设：具有更强的大党偏差的选举制度会表现出显著更高的政治极化水平。