Clustering and Inference for Ballot Models for VRA Analysis

Music City Center 

Analysis of alternative election systems often requires modeling of ballots in settings where the available data is not perfectly aligned with the potential mechanism. In this talk I will discuss both empirical and theoretical questions that arise while doing this modeling, motivated by applications of state-level voting rights act legislation. In particular, I will consider questions in supervised learning including how past data can provide information about likely impacts of a new voting system and what the related inference problem looks like for recently introduced slate-based models. I will also discuss the related unsupervised problem of clustering ballots from preference profiles and the identification of party membership.

Voting Methods

Ranked Ballots

Clustering

Statistical Consulting 

Section on Statistical Consulting