Identifying Possible Winners in Ranked Choice Voting Elections with Outstanding Ballots

TL;DR

This paper introduces an algorithm to efficiently identify potential winners in ranked-choice voting elections using partial ballot data, enhancing transparency and speed in election result reporting.

Contribution

The paper presents a novel algorithm that computes possible winners from incomplete ballots, demonstrated on NYC primary election data, improving election transparency.

Findings

Algorithm narrows down possible winners significantly

Potential to identify winners on election night

Enhances transparency with partial ballot data

Abstract

Several election districts in the US have recently moved to ranked-choice voting (RCV) to decide the results of local elections. RCV allows voters to rank their choices, and the results are computed in rounds, eliminating one candidate at a time. RCV ensures fairer elections and has been shown to increase elected representation of women and people of color. A main drawback of RCV is that the round-by-round process requires all the ballots to be tallied before the results of an election can be calculated. With increasingly large portions of ballots coming from absentee voters, RCV election outcomes are not always apparent on election night, and can take several weeks to be published, leading to a loss of trust in the electoral process from the public. In this paper, we present an algorithm for efficiently computing possible winners of RCV elections from partially known ballots and evaluate it on data from the recent New York City Primary elections. We show that our techniques allow to significantly narrow down the field of possible election winners, and in some case identify the winner as soon as election night despite a number of yet-unaccounted absentee ballots, providing more transparency in the electoral process.