Electoral Systems Simulator: An Open Framework for Comparing Electoral Mechanisms Across Voter Distribution Scenarios

Researcher Sumit Mukherjee has released 'electoral_sim,' a comprehensive open-source Python framework designed to rigorously compare electoral systems. The tool models voters and candidates as points in a two-dimensional ideological space, generating sincere ballot preferences based on Euclidean distances. It then simulates elections using seven established mechanisms: plurality, ranked-choice (RCV), approval, score, Condorcet methods, and two proportional representation systems. The core metric for comparison is the Euclidean distance between the election winner and the geometric median of the voter distribution—essentially measuring how well the outcome reflects the central consensus of the electorate.

The framework evaluates these systems across a spectrum of realistic scenarios, from unified, unimodal electorates to sharply polarized, bimodal populations. Each scenario undergoes 200 Monte Carlo trials to assess the stability and performance of each voting rule. As a demonstration of extensibility, Mukherjee also implemented a novel, hypothetical 'Boltzmann softmax' mechanism. This system distributes each voter's influence probabilistically across candidates and serves as a theoretical benchmark, showing an approximate upper bound for centroid-seeking performance rather than a practical proposal. All code is publicly available, offering researchers and policymakers a standardized toolkit to move beyond anecdotal debates about electoral reform with computational evidence.