Distances Between Top-Truncated Elections of Different Sizes
🤖 AI Summary
Existing approaches to visualizing election data are constrained by assumptions of fixed candidate and voter sets, as well as complete preference rankings, rendering them ill-suited for real-world scenarios involving heterogeneous scales and top-truncated preferences. This work extends the election map framework to such generalized settings for the first time, introducing a distance-based modeling approach, an algorithm tailored to handle truncated ballots, and a corresponding topological visualization technique. Extensive experiments on large-scale real-world election datasets from the Preflib repository demonstrate that the proposed method effectively overcomes the limitations of prior frameworks, substantially enhancing both the applicability and scalability of visual election analysis.
Application Category
📝 Abstract
The map of elections framework is a methodology for visualizing and analyzing election datasets. So far, the framework was restricted to elections that have equal numbers of candidates, equal numbers of voters, and where all the (ordinal) votes rank all the candidates. We extend it to the case of elections of different sizes, where the votes can be top-truncated. We use our results to present a visualization of a large fragment of the Preflib database.
Problem
Research questions and friction points this paper is trying to address.
elections
top-truncated votes
different sizes
map of elections
Preflib
Innovation
Methods, ideas, or system contributions that make the work stand out.
map of elections
top-truncated votes
election visualization
Preferential voting
election data analysis
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