19 research outputs found
A General Approach for Predicting the Behavior of the Supreme Court of the United States
Building on developments in machine learning and prior work in the science of
judicial prediction, we construct a model designed to predict the behavior of
the Supreme Court of the United States in a generalized, out-of-sample context.
To do so, we develop a time evolving random forest classifier which leverages
some unique feature engineering to predict more than 240,000 justice votes and
28,000 cases outcomes over nearly two centuries (1816-2015). Using only data
available prior to decision, our model outperforms null (baseline) models at
both the justice and case level under both parametric and non-parametric tests.
Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level
and 71.9% at the justice vote level. More recently, over the past century, we
outperform an in-sample optimized null model by nearly 5%. Our performance is
consistent with, and improves on the general level of prediction demonstrated
by prior work; however, our model is distinctive because it can be applied
out-of-sample to the entire past and future of the Court, not a single term.
Our results represent an important advance for the science of quantitative
legal prediction and portend a range of other potential applications.Comment: version 2.02; 18 pages, 5 figures. This paper is related to but
distinct from arXiv:1407.6333, and the results herein supersede
arXiv:1407.6333. Source code available at
https://github.com/mjbommar/scotus-predict-v
Distance Measures for Dynamic Citation Networks
Acyclic digraphs arise in many natural and artificial processes. Among the
broader set, dynamic citation networks represent a substantively important form
of acyclic digraphs. For example, the study of such networks includes the
spread of ideas through academic citations, the spread of innovation through
patent citations, and the development of precedent in common law systems. The
specific dynamics that produce such acyclic digraphs not only differentiate
them from other classes of graphs, but also provide guidance for the
development of meaningful distance measures. In this article, we develop and
apply our sink distance measure together with the single-linkage hierarchical
clustering algorithm to both a two-dimensional directed preferential attachment
model as well as empirical data drawn from the first quarter century of
decisions of the United States Supreme Court. Despite applying the simplest
combination of distance measures and clustering algorithms, analysis reveals
that more accurate and more interpretable clusterings are produced by this
scheme.Comment: 7 pages, 5 figures. Revision: Added application to the network of the
first quarter-century of Supreme Court citations. Revision 2: Significantly
expanded, includes application on random model as wel