Analysis of Voting Systems Using Monte Carlo Simulation

Fall 2017 Analytics Project
Team Members: Peter Butler, Jordan Fan, Xuhui Zhou, Kaushik Ravi, Jiaqi Guo

The goal of this project was to examine the effectiveness of various voting systems via Monte Carlo simulation. We used simulated data by randomly generating utility values for each voter-candidate pair to make many datasets of “voters,” and then we ran every type of election on these datasets to see which system is most likely to elect the socially preferred candidate. We found that the score voting system is the most effective, although plurality is least vulnerable to strategic manipulation.