Dietrich Stoyan writes:
I asked the IMS people for an expert in statistics of voting/elections and they wrote me your name. I am a statistician, but never worked in the field voting/elections. It was my son-in-law who asked me for statistical theories in that field.
He posed in particular the following problem:
The aim of the voting is to come to a ranking of c candidates. Every vote is a permutation of these c candidates. The problem is to have probability distributions in the set of all permutations of c elements.
Are there theories for such distributions?
I should be very grateful for a fast answer with hints to literature. (I confess that I do not know your books.)
My reply: Rather than trying to model the ranks directly, I’d recommend modeling a latent continuous outcome which then implies a distribution on ranks, if the ranks are of interest. There are lots of distributions of c-dimensional continuous outcomes. In political science, the usual way to start is to model the positions of the candidates and of the voters, and then to have a model mapping relative positions to relative preferences.
Huang and Guestrin, "Uncovering the Riffled Independence Structure of Rankings."
Even if the paper itself isn't quite what you're looking for, the references in Section 2 might be a good place to start exploring the literature.
There's also a whole area of machine learning devoted to "learning to rank." (e.g.)
there is a book about ranks <a>"Analyzing and Modeling Rank Data" by John Marden. Donald Saari has also written <a>on voting.