Ranking with multiple types of pairwise comparisons

The task of ranking individuals or teams, based on a set of comparisons between pairs, arises in various contexts, including sporting competitions and the analysis of dominance hierarchies among animals and humans. Given data on which competitors beat which others, the challenge is to rank the competitors from best to worst. Here we study the problem of computing rankings when there are multiple, potentially conflicting types of comparison, such as multiple types of dominance behaviours among animals. We assume that we do not know a priori what information each behaviour conveys about the ranking, or even whether they convey any information at all. Nonetheless, we show that it is possible to compute a ranking in this situation and present a fast method for doing so, based on a combination of an expectation-maximization algorithm and a modified Bradley-Terry model. We give a selection of example applications to both animal and human competition.

Automated summary

Explored the problem of computing rankings when there are multiple conflicting types of comparison and proposed a fast method based on an expectation-maximization algorithm and a modified Bradley-Terry model.