Banzhaf Power in Hierarchical Voting Games

TL;DR

This paper introduces Extended BPI (EBPI), a generalized measure of voting power applicable to unbalanced hierarchical voting games, improving computational efficiency and applicability to real-world scenarios like sentiment analysis.

Contribution

It generalizes BPI to unbalanced voting games through EBPI and demonstrates its computational advantages and practical application in language sentiment modeling.

Findings

EBPI simplifies to BPI in balanced games

Decomposition of BPI in unbalanced games uses EBPI at each hierarchy level

Significant runtime improvements in sentiment analysis application

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.