
TL;DR
The paper introduces a 'map of elections' framework that visualizes elections in 2D space based on similarity measures, aiding analysis of synthetic and real election datasets.
Contribution
It proposes a novel framework for visualizing elections in 2D space, including a polynomial-time similarity measure and multiple visualization techniques.
Findings
The map accurately reflects election similarities.
Coloring reveals insights into election outcomes and algorithm performance.
The framework is effective for analyzing synthetic and real elections.
Abstract
Our main contribution is the introduction of the map of elections framework. A map of elections consists of three main elements: (1) a dataset of elections (i.e., collections of ordinal votes over given sets of candidates), (2) a way of measuring similarities between these elections, and (3) a representation of the elections in the 2D Euclidean space as points, so that the more similar two elections are, the closer are their points. In our maps, we mostly focus on datasets of synthetic elections, but we also show an example of a map over real-life ones. To measure similarities, we would have preferred to use, e.g., the isomorphic swap distance, but this is infeasible due to its high computational complexity. Hence, we propose polynomial-time computable positionwise distance and use it instead. Regarding the representations in 2D Euclidean space, we mostly use the Kamada-Kawai algorithm,…
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Taxonomy
TopicsGame Theory and Voting Systems · Electoral Systems and Political Participation · Internet Traffic Analysis and Secure E-voting
