Effect of Electoral Seat Bias on Political Polarization: A Computational Perspective

2026-02-08

AI Summary

This study investigates whether electoral systems that bias seat allocation in favor of large parties exacerbate political polarization. By integrating a spatial voting model with multiple realistic voter behavior mechanisms—such as bandwagon effects and strategic voting—into a multi-period agent-based framework, and employing Monte Carlo simulations, the research provides the first systematic computational quantification of the causal effect of electoral bias on polarization. Using the Mehlahff polarization index for evaluation, the analysis reveals that majoritarian systems like plurality rule and party-list methods such as the Jefferson–D’Hondt formula, which amplify large-party advantages, significantly intensify political polarization. These findings offer critical theoretical insights for the design of electoral institutions and their implications for democratic stability.

Technology Category

• Game Theory and Economic Paradigms: Social Choice / Voting
• Knowledge Representation and Reasoning: Preferences
• Multiagent Systems: Mechanism Design

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems

Abstract

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.

Problem

• electoral seat bias
• political polarization
• electoral systems
• large-party bias
• computational modeling

Innovation

• agent-based modeling
• Monte Carlo simulation
• electoral seat bias
• political polarization
• voter behavior dynamics