Geometric Comparisons of Electoral Rules Under Feedback

TL;DR

This paper introduces geometric primitives to compare electoral rules based on how they influence voter and candidate polarization, revealing inherent tradeoffs through theoretical analysis and extensive simulations.

Contribution

It develops a geometric framework for analyzing electoral rules, demonstrating the tradeoff between voter polarization and candidate dispersion, supported by comprehensive simulation results.

Findings

Rules minimizing winner radius increase candidate dispersion.

Convex-combination rules balance polarization and dispersion.

Tradeoffs are confirmed through extensive simulation across diverse settings.

Abstract

We study how electoral rules shape polarization dynamics when voters and candidates both adapt to repeated election outcomes. We introduce two geometric primitives for comparing rules under this feedback: the \emph{winner radius} $R_t = \max_i \|x_i - w^{(t)}\|$, the distance from the winner to the farthest voter, and the \emph{supporter centroid radius} $S_t = \max_j \|c_j - s_j^{(t)}\|$, the largest gap between any candidate and their support base. We show that $R_t$ controls a one-step contraction bound on voter disagreement and $S_t$ plays the analogous role for candidate dispersion, and that these two objectives are in tension. Rules that reduce $R_t$ tend to increase $S_t$, and vice versa. A winner close to the voter median does not resolve the tension, since proximity to the median and proximity to the Chebyshev center are different objectives. We use this framing to organize a simulation study across seven standard electoral rules and one convex-combination benchmark, comprising 1000+ runs across diverse electorate profiles, voter mechanisms, and camp-balance settings. The empirical results confirm the theoretical tradeoff: winner-take-all rules achieve small $S_t$ at the cost of large $R_t$ and weaker voter depolarization, while convex-combination rules reverse this. An oracle comparison further shows that minimizing $R_t$ per step and minimizing voter disagreement per step are distinct objectives with different long-run consequences for both voter and candidate dynamics.