This paper written by CLS Blog Co-Founder Daniel Katz and Derek Stafford from the University of Michigan Department of Political Science representes an initial foray into Computational Legal Studies by the graduate students here at the University of Michigan Center for the Study of Complex Systems. The full paper contains a number of interesting visualizations where we draw various federal judges together on the basis of their shared law clerks (1995-2004). The screen print above is a zoom very center of the center of the network. Yellow Nodes represent Supreme Court Justices, Green Nodes represent Circuit Court Justices, Blue Nodes represent Circuit Court Justices. Here is a wide shot of the broader network visualized using the Kamada-Kawai visualization algorithm:
Here is the abstract: Scholars have long asserted that social structure is an important feature of a variety of societal institutions. As part of a larger effort to develop a fully integrated model of judicial decision making, we argue that social structure-operationalized as the professional and social connections between judicial actors-partially directs outcomes in the hierarchical federal judiciary. Since different social structures impose dissimilar consequences upon outputs, the precursor to evaluating the doctrinal consequences that a given social structure imposes is a descriptive effort to characterize its properties. Given the difficulty associated with obtaining appropriate data for federal judges, it is necessary to rely upon a proxy measure to paint a picture of the social landscape. In the aggregate, we believe the flow of law clerks reflects a reasonable proxy for social and professional linkages between jurists. Having collected available information for all federal judicial law clerks employed by an Article III judge during the “natural” Rehnquist Court (1995-2004), we use these roughly 19,000 clerk events to craft a series of network based visualizations. Using network analysis, our visualizations and subsequent analytics provide insight into the path of peer effects in the federal judiciary. For example, we find the distribution of “degrees” is highly skewed implying the social structure is dictated by a small number of socially prominent actors. Using a variety of centrality measures, we identify these socially prominent jurists. Next, we draw from the extant complexity literature and offer a possible generative process responsible for producing such inequality in social authority. While the complete adjudication of a generative process is beyond the scope of this article, our results contribute to a growing literature documenting the highly-skewed distribution of authority across the common law and its constitutive institutions.
Research in the academic world suffers from the “hammer problem” – that is, the methods we use are often those that we have in our toolbox, not necessarily those that we should be using. This is especially true in computational social science, where we often attempt to directly import well-developed methods from the hard sciences.
To prove the point, I’d like to highlight one example we’ve come across in our research. In Leicht et al’s Large-scale structure of time evolving citation networks, the authors apply two methods to a simplified representation of the United States Supreme Court citation network. Both of these methods rely on complicated statistical algorithms and require iterative non-linear system solvers. However, the results are consistent, and they detect “events” around 1900, 1940, and 1970.
One first-order alternative to detecting significant “events” in the Court would be to count citations. One might suspect, for instance, that the formation or destruction of law might go hand-in-hand with an acceleration or deceleration in the rate of citation. Such a method is purely conjectural, but costs much less to implement than the methods discussed above.
This figure shows the number of outgoing citations per year in blue, as well as the ten-year moving average in purple. The plot shows jumps that coincide very well with the plot from Leicht, et. al. Thus, although only a first-order approximation to the underlying dynamics, this method would lead historians down a similar path with much less effort.
This example, though simple, is one that really hits home for me. After a week of struggling to align interpretations and methods, this plot convinced me more than any eigenvector or Lagrangian system. Perhaps more importantly, unlike the above methods, you can explain this plot to a lay audience in a fifteen minute talk.
This article in a recent issue of Science Magazine— authored by some of the leaders in field— highlights some of the possibilities of and perils associated with a computational revolution in the social sciences. We believe it is a worthwhile read….
In the days and weeks ahead, we hope to outline why we believe the application of a computational and complexity informed approach to legal studies will serve as a useful method to consider a wide class of substantive questions. Standing at the intersection of a variety of fields including computer science, applied mathematics, physics, political science, social network analysis as well as others, we hope scholars will be able to leverage relevant techniques to help enrich positive legal theory.
As a entry point, we will highlight relevant developments to date in this new field–including our own work as well as the work of others. So we offer this initial post to say ‘Hello World’ with a promise of more to come….
Welcome to the Computational Legal Studies blog! We will be organizing behind the scenes in the short term, but check back soon for original content on the computational study of law and the application of complexity theory to legal scholarship. In the meantime… Happy St. Patrick’s Day!