Experiments in Computational Social Choice Using Maps of Elections

We present the “map of elections” framework for designing and analyzing numerical experiments on elections in computational social choice, as well as for analyzing the space of elections. The idea is to take a set of elections, compute  distances between them, and present them as points on a plane, whose distances resemble those between the elections. The map can be used to analyze the nature of elections and to visualize results of experiments.

We will show the main components of the framework, including different ways of computing distances between elections, sources of election data, and algorithms for embedding elections in 2D space. Next, we will show a number of use cases of the framework. This includes a demonstration how the map can be used to plan, analyze, and evaluate experiments without resorting to computing averages etc. Moreover, we will use the map for analyzing and classifying synthetic and real-life data. Finally, we will show various extensions of the framework.