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Computational geometry and the U.S. Supreme Court

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  • Giansiracusa, Noah
  • Ricciardi, Cameron

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.

Suggested Citation

  • Giansiracusa, Noah & Ricciardi, Cameron, 2019. "Computational geometry and the U.S. Supreme Court," Mathematical Social Sciences, Elsevier, vol. 98(C), pages 1-9.
    • Handle: RePEc:eee:matsoc:v:98:y:2019:i:c:p:1-9
    DOI: 10.1016/j.mathsocsci.2018.12.001
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    References listed on IDEAS

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