Competing Approaches to Predicting Supreme Court Decision Making
Andrew D.
Martin
a1
, Kevin M.
Quinn
a2
, Theodore W.
Ruger
a3
and Pauline T.
Kim
a4
a1 Andrew D. Martin (admartin@wustl.edu) is
associate professor of political science at Washington University,
St. Louis
a2 Kevin Quinn (kquinn@latte.harvard.edu) is
assistant professor at Harvard University, Department of
Government
a3 Theodore W. Ruger (truger@law.upenn.edu) is
assistant professor at the University of Pennsylvania Law
School
a4 Pauline T. Kim (kim@wulaw.wustl.edu) is
professor of law at Washington University School of Law
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Political scientists and legal academics have long scrutinized the
U.S. Supreme Court's work to understand what motivates the
justices. Despite significant differences in methodology, both
disciplines seek to explain the Court's decisions by focusing on
examining past cases. This retrospective orientation is surprising. In
other areas of government, for example, presidential elections and
congressional decision making, political scientists engage in
systematic efforts to predict outcomes, yet few have done this for
court decisions. Legal academics, too, possess expertise that should
enable them to forecast legal events with some accuracy. After all, the
everyday practice of law requires lawyers to predict court decisions in
order to advise clients or determine litigation strategies. a
Footnotesa The authors thank Michael Cherba, Nancy Cummings,
David Dailey, Alison Garvey, Nick Hershman, and Robin Rimmer for their
assistance. Their project is supported in part by National Science
Foundation grants SES-0135855 and SES 0136679. The foundation bears no
responsibility for the results or conclusions.
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