Banzhaf Power in Hierarchical Voting Games
John Randolph, Denizalp Goktas, Amy Greenwald
TL;DR
The paper addresses the computational challenge of the Banzhaf power index (BPI) in hierarchical voting games by identifying balance as the key property enabling efficient MBPI decompositions. It generalizes the framework with Extended BPI (EBPI) and its hierarchical counterpart MEBPI to handle unbalanced games, proving that MEBPI yields the same BPI as direct computation while maintaining efficiency on hierarchical structures. The authors demonstrate substantial runtime improvements and maintain accuracy through experiments on Slovenian National Council-style hierarchies and sentiment analysis tasks framed as hierarchical voting, where language compositionality is imperfect. The results show EBPI and MEBPI as practical proxies for BPI in unbalanced or complex hierarchies, offering scalable analysis for large-scale voting-like systems and language applications. This work broadens the applicability of power-index analysis to unbalanced and real-world hierarchical settings, with implications for political science, ensemble methods, and NLP interpretability.
Abstract
The Banzhaf Power Index (BPI) is a method of measuring the power of voters in determining the outcome of a voting game. Some voting games exhibit a hierarchical structure, including the US electoral college and ensemble learning methods; we call such games hierarchical voting games. It is generally understood that BPI in hierarchical voting games can be computed via a recursive decomposition of the hierarchy, which can substantially reduce the calculation's complexity. We identify a key (previously undocumented) assumption on which this decomposition is based, namely balance, meaning one group of voters has enough votes to win whenever the complementary group of voters does not, and vice versa. We then introduce a generalization of BPI that we call Extended BPI (EBPI) for all voting games, including those that are not balanced, which simplifies to BPI in balanced games. We show that BPI in unbalanced hierarchical voting games decomposes in terms of EBPI at each level in the hierarchy, which yields computational savings analogous to those achieved in the balanced case. As a sample application, we take advantage of the compositionality of language, and model the impact of individual words on a sentence's sentiment as a voting game. As the complement of a phrase in a sentence does not necessarily have the opposite sentiment, this voting game is unbalanced and requires our decomposition of BPI in terms of EBPI. Our results suggest that EBPI is an effective proxy for BPI (because the meaning of a sentence is not always 100\% compositional), and demonstrate a dramatic improvement in run time.