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arXiv · 1810.11704

Computational geometry and the U.S. Supreme Court

Noah Giansiracusa·Cameron Ricciardi
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Abstract

We use the United States Supreme Court as an illuminative context in which to discuss three different spatial voting preference models: an instance of the widely used single-peaked preferences, and two models that are more novel in which vote outcomes have a strength in addition to a location. We introduce each model from a formal axiomatic perspective, briefly discuss practical motivation for each in terms of judicial behavior, prove mathematical relationships among the voting coalitions compatible with each model, and then study the two-dimensional setting by presenting computational tools for working with the models and by exploring these with judicial voting data from the Supreme Court.

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BibTeXRIS

Noah Giansiracusa, Cameron Ricciardi. 2018-10-27. Computational geometry and the U.S. Supreme Court. https://arxiv.org/abs/1810.11704

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