  Ranked Choice Voting (RCV) and Single Transferable Voting (STV) are widely
valued; but are complex to understand due to intricate per-round vote
transfers. Questions like determining how far a candidate is from winning or
identifying effective election strategies are computationally challenging as
minor changes in voter rankings can lead to significant ripple effects - for
example, lending support to a losing candidate can prevent their votes from
transferring to a more competitive opponent. We study optimal strategies -
persuading voters to change their ballots or adding new voters - both
algorithmically and theoretically. Algorithmically, we develop efficient
methods to reduce election instances while maintaining optimization accuracy,
effectively circumventing the computational complexity barrier. Theoretically,
we analyze the effectiveness of strategies under both perfect and imperfect
polling information. Our algorithmic approach applies to the ranked-choice
polling data on the US 2024 Republican Primary, finding, for example, that
several candidates would have been optimally served by boosting another
candidate instead of themselves.
