Utilization of Machine Learning to Simulate the Implementation of Instant Runoff Voting

In election years when the popular vote winner and Electoral College winner differ, such as in the 2016 presidential election, there tends to be an increase in discussions about alternative voting strategies. Ranked choice voting is a strategy that has been discussed and is currently used in approximately fourteen cities across the United States and in six states for special elections and overseas ballots. Ranked choice voting (RCV), sometimes called Instant Runoff Voting (IRV), is a system of voting in which voters are allowed to rank the candidates. If no candidate wins over fifty percent of the vote, the election automatically goes to another round. The candidate with the least support is eliminated and their votes are redistributed to the voters’ next choice. This process of elimination and redistribution continues until a candidate receives a majority of the vote. In this paper, we use predictive modeling strategies and simulation to investigate the potential implications of employing ranked choice voting in a presidential election using the 2016 presidential election as a case study.