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

How can we probabilistically predict the winner in a ranked-choice election without all ballots being counted? We study and 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 algorithm is effective for elections with a small number of candidates 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 elections.