Ahead of the Count: An Algorithm for Probabilistic Prediction of Instant Runoff (IRV) Elections

TL;DR

This paper presents a novel algorithm for probabilistically predicting IRV election outcomes using partial vote data, enabling real-time election night modeling and recount predictions.

Contribution

The study introduces a new algorithm that calculates probabilities of all possible IRV elimination sequences from partial vote distributions, supporting real-time election predictions.

Findings

Effective for elections with up to five candidates

Fast execution suitable for real-time predictions

Validated with real data from 2022 Alaska elections

Abstract

How can we probabilistically predict the winner in a ranked-choice election without all ballots being counted? In this study, we introduce a novel algorithm designed to predict outcomes in Instant Runoff Voting (IRV) elections. The algorithm takes as input a set of discrete probability distributions describing vote totals for each candidate ranking and calculates the probability that each candidate will win the election. In fact, we calculate all possible sequences of eliminations that might occur in the IRV rounds and assign a probability to each. The discrete probability distributions can be arbitrary and, in applications, could be measured empirically from pre-election polling data or from partial vote tallies of an in-progress election. The algorithm is effective for elections with a small number of candidates (five or fewer), with fast execution on typical consumer computers. The run-time is short enough for our method to be used for real-time election night modeling where new predictions are made continuously as more and more vote information becomes available. We demonstrate the algorithm in abstract examples, and also using real data from the 2022 Alaska state elections to simulate election-night predictions and also predictions of election recounts.