Justice blocks and predictability of US Supreme Court votes (1210.4768v1)

Abstract: Successful attempts to predict judges' votes shed light into how legal decisions are made and, ultimately, into the behavior and evolution of the judiciary. Here, we investigate to what extent it is possible to make predictions of a justice's vote based on the other justices' votes in the same case. For our predictions, we use models and methods that have been developed to uncover hidden associations between actors in complex social networks. We show that these methods are more accurate at predicting justice's votes than forecasts made by legal experts and by algorithms that take into consideration the content of the cases. We argue that, within our framework, high predictability is a quantitative proxy for stable justice (and case) blocks, which probably reflect stable a priori attitudes toward the law. We find that U. S. Supreme Court justice votes are more predictable than one would expect from an ideal court composed of perfectly independent justices. Deviations from ideal behavior are most apparent in divided 5-4 decisions, where justice blocks seem to be most stable. Moreover, we find evidence that justice predictability decreased during the 50-year period spanning from the Warren Court to the Rehnquist Court, and that aggregate court predictability has been significantly lower during Democratic presidencies. More broadly, our results show that it is possible to use methods developed for the analysis of complex social networks to quantitatively investigate historical questions related to political decision-making.

Analysis of Justice Blocks and Predictability of U.S. Supreme Court Votes

The paper "Justice Blocks and Predictability of U.S. Supreme Court Votes" by Roger Guimerà and Marta Sales-Pardo presents a comprehensive exploration of the predictability of judicial decisions within the U.S. Supreme Court. Utilizing methods typically associated with complex social network analysis, the authors investigate whether it is feasible to predict a justice's vote through the votes of the other justices without considering case content. This methodological approach reveals substantial findings about the stability and predictability of justice blocks.

Guimerà and Sales-Pardo's paper establishes that methods designed to uncover concealed associations in complex networks outperform legal experts and algorithms focused on case content in predicting justices' votes. Such an approach posits high predictability as indicative of stable justice blocks, reflecting potentially consistent attitudes toward the law among the justices. These voting blocks deviate significantly from those in an "ideal" court composed of independent, unbiased judges. Moreover, this predictability is notably pronounced in closely divided 5-4 decisions, revealing the persistent stability of justice blocks under such conditions.

One significant outcome revealed through the paper's analyses is the decrease in the predictability of justice votes from the Warren Court to the Rehnquist Court over a fifty-year span. Interestingly, aggregate court predictability appears markedly lower during Democratic presidencies—a finding suggesting the influence of broader political contexts on judicial predictability. These findings highlight the utilitarian value of complex network methods in historical-political analysis, especially with regard to examining the underlying patterns in political decision-making frameworks like the Supreme Court.

Employing stochastic block models to assess justice predictability, the authors offer insights into justice behavior, decisional biases, and the evolution of judicial predictability. The predictability gap noted between actual and ideal court scenarios accentuates the presence of a voting block structure, which is not accounted for in conventional individual vote models. In real courts, the stochastic block model predicts 83% accuracy, surpassing predictions by legal experts and content-focused algorithms, which stood at around 67.9% and 66.7%, respectively.

Furthermore, the paper reveals variations in the relative predictability of individual justices, where figures like Marshall exhibit a higher degree of predictability compared to others. This attribute, however, did not correlate straightforwardly with the justices’ political leanings, highlighting complex interaction patterns beyond simple ideological categorizations. The paper also posits that Democrat-nominated and Republican-nominated justices show no significant difference in predictability. However, predictability under Democratic presidencies emerged distinctly lower than under Republican ones—an observation linked to the timing and political landscape surrounding the Kennedy presidency.

In conclusion, Guimerà and Sales-Pardo’s work provides a rigorous quantitative framework for examining the dynamics of judicial decision-making within the Supreme Court, pushing forward the understanding of attitudinal coherence among justices. The findings signify potential avenues for future research to explore the interaction between judicial predictability and political influences, helping establish a more nuanced understanding of judicial behavior and its implications. The methodological confluence of social network analysis and judicial decision forecasting portrayed in this paper opens pathways for extending such analytical approaches within other domains in political science and legal studies.