Abstract

http://ssrn.com/abstract=2463244
 


 



Predicting the Behavior of the Supreme Court of the United States: A General Approach


Daniel Martin Katz


Illinois Tech - Chicago Kent College of Law

Michael James Bommarito II


Bommarito Consulting, LLC

Josh Blackman


Houston College of Law

July 21, 2014


Abstract:     
Building upon developments in theoretical and applied machine learning, as well as the efforts of various scholars including Guimera and Sales-Pardo (2011), Ruger et al. (2004), and Martin et al. (2004), we construct a model designed to predict the voting behavior of the Supreme Court of the United States. Using the extremely randomized tree method first proposed in Geurts, et al. (2006), a method similar to the random forest approach developed in Breiman (2001), as well as novel feature engineering, we predict more than sixty years of decisions by the Supreme Court of the United States (1953-2013). Using only data available prior to the date of decision, our model correctly identifies 69.7% of the Court’s overall affirm/reverse decisions and correctly forecasts 70.9% of the votes of individual justices across 7,700 cases and more than 68,000 justice votes. Our performance is consistent with the general level of prediction offered by prior scholars. However, our model is distinctive as it is the first robust, generalized, and fully predictive model of Supreme Court voting behavior offered to date. Our model predicts six decades of behavior of thirty Justices appointed by thirteen Presidents. With a more sound methodological foundation, our results represent a major advance for the science of quantitative legal prediction and portend a range of other potential applications, such as those described in Katz (2013).

Number of Pages in PDF File: 17

Keywords: United States Supreme Court, Machine Learning, Law and Social Science, Quantitative Legal Prediction

JEL Classification: C45, K40


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Date posted: July 9, 2014 ; Last revised: July 27, 2014

Suggested Citation

Katz, Daniel Martin and Bommarito, Michael James and Blackman, Josh, Predicting the Behavior of the Supreme Court of the United States: A General Approach (July 21, 2014). Available at SSRN: http://ssrn.com/abstract=2463244 or http://dx.doi.org/10.2139/ssrn.2463244

Contact Information

Daniel Martin Katz (Contact Author)
Illinois Tech - Chicago Kent College of Law ( email )
565 W. Adams St.
Chicago, IL 60661-3691
United States
HOME PAGE: http://www.danielmartinkatz.com/

Michael James Bommarito II
Bommarito Consulting, LLC ( email )
Troy, MI 48098
United States
16464503387 (Phone)
HOME PAGE: http://bommaritollc.com/
Josh Blackman
Houston College of Law ( email )
1303 San Jacinto Street
Houston, TX 77002
United States
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