Sen. Sheldon Whitehouse on the Senate Floor Discussing the Length of the Health Care Bill and Citing Harry Potter Number [Via Think Progress]

Full Story over at Think Progress.  Our original post on the length of HR 3962 is here.  The subsequent NY Times Article on the Length of HR 3962 is here. Enjoy!

Dynamic Animation of the East Anglia Climate Research Unit Email Network



Click on this icon to view the Movie in Full Screen Mode!Picture 4


In our prior post analyzing the email database of Climate Research Unit at the University of East Anglia, we aggregated all emails over the relevant 1997-2009 time period into a single static visualization. Specifically, to build the network, we processed every email in the leaked data. Each email contains a sender and at least one recipient on the To:, Cc:, or Bcc: line.

One obvious shortcoming associated with producing a static snapshot for data set, is that it often obscures the time evolving dynamics of interaction which produced the full graph.  To generate a dynamic picture, it is necessary to collect time stamped network data. In the current case, this required acquisition of the date field for each of the emails. With this information, we used the same underlying data to generate a dynamic network animation for the 1997-2009 time window.


Consistent with the approach offered in our prior visualization, each node represents an individual within the email dataset while each connection reflects the weighted relationship between those individuals. The movie posted above features the date in the upper left.  As time ticks forward, you will notice that the relative social relationships between individuals are updated with each new batch of emails.  In some periods, this updating has significant impact upon the broader network topology and at other time it imposes little structural consequences.

In each period, both new connections as well as new communications across existing connections are colored teal while the existing and dormant relationships remain white.  Among other things, this is useful because it identifies when a connection is established and which interactions are active at any given time period.


We have two separate versions of the movie.  The version above is a shorter version where roughly 13 years is displayed in under 2 minutes.  In the coming days, we will have a longer version of the movie which ticks a one email at a time. In both versions, each frame is rendered using the Kamada-Kawai layout algorithm. Then, the frames are threaded together using linear interpolation.


Issues of selection of confront many researchers. Namely, given the released emails are only a subset of the broader universe of emails authored over the relevant time window, it is important to remember that the data has been filtered and the impact of this filtration can not be precisely determined. Notwithstanding this issue, our assumption is that every email from a sender to a recipient represents a some level of relationship between them.  Furthermore, we assume that more emails sent between two people generally indicates a stronger relationship between those individuals.


In our academic scholarship, we have confronted questions of dimensionality in network data. Simply put, analyzing network data drawn from high dimensional space can be really thorny. In the current context, a given email box likely contains emails on lots of subjects and reflects lots of people not relevant to the specific issue in question. Again, while we do not specifically know the manner in which the filter was applied, it is certainly possible that the filter actually served to mitigate issues of dimensionality.


For those interested in searching the emails, the NY Times directs the end user to

Well Formed Eigenfactor.Org–Wonderful Visualization of CrossDisciplinary Fertilization, Information Flow & The Structure of Science [Repost]


Given our interest in both interdisciplinary scholarship and the spread of ideas, we wanted to highlight one of our favorite projects– Here is basic documentation from their website.  There are also links to academic papers offering far more detailed documentation for the data and algorithm choice.  In particular, read Martin Rosvall and Carl T. Bergstrom, Maps of Random Walks on Complex NetworksProc. of the Nat. Academy of Sci. 105:1118-1123 (2007).  The above visualizations are written in Flare by Moritz Stefaner. Click on the slide above to reach these interactive visualizations. These mapping offer reveal the reach of various publications across disciplines–some are insular and others have incredible reach.  The inner rings are journals and the outer rings are the host disciplines. Enjoy!

Google Wave — A Promising Platform for Real-Time Collaboration


Also from the good folks at Google Scholar comes caselaw and patents together with metadata, page tags and a nice “how cited” feature.  Here is the announcement from the GoogleBlog. Useful analysis available at Legal Informatics Blog, Just in Case and Internet for Lawyers. Enjoy!

"Sink Method" Poster for Conference on Empirical Legal Studies (CELS 2009 @ USC)

Sinks Poster

As we mentioned in previous posts, Seadragon is a really cool product. Please note load times may vary depending upon your specific machine configuration as well as the strength of your internet connection. For those not familiar with how to operate it please see below. In our view, the Full Screen is best the way to go ….

Conference on Empirical Legal Studies @ USC Law School

CELS 2009

Mike and I are in route to the 2009 Conference on Empirical Legal Studies (CELS) at USC Law School.  This post is actually coming to you from 32,000 feet on GoGo Wireless.  I still cannot get over the idea of being on wireless from a moving airplane.  We live in extraordinary times!

Law Professoriate Poster for Conference on Empirical Legal Studies (CELS 2009 @ USC)


As we mentioned in previous posts, Seadragon is a really cool product. Please note load times may vary depending upon your specific machine configuration as well as the strength of your internet connection. For those not familiar with how to operate it please see below. In our view, the Full Screen is best the way to go ….

Statistical Time Machines


So, I was a bit late on this … However, it is a really cool idea and thus I want to flag it for those who might have missed it.  As covered over at SCOTUS Blog and ELS Blog, the November 12th Wall Street Journal features a story entitled “Statistical Time Travel Helps to Answer What-Ifs.”  Of interest to legal scholars, Professors Andrew Martin and Kevin Quinn discuss a series of what-ifs including how today’s Supreme Court would have voted on Roe v. Wade … Check it out!

Katz & Bommarito in the New York Times Discussing H.R. 3962

NYT Rx Blog

If you click through on the link above you will be directed to the New York Times Rx Blog.  The full version of the article appears online while a shorter version appeared in today’s print edition. For those viewing the print edition, the story is located on page A20. This website is mentioned in both versions of the story!

Hustle and Flow: A Social Network Analysis of the American Federal Judiciary [Repost from 3/25]

Zoom on Network

Together with Derek Stafford from the University of Michigan Department of Political Science, Hustle and Flow: A Social Network Analysis of the American Federal Judiciary represents our initial foray into Computational Legal Studies. 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 District Court Justices.

There exist many high quality formal models of judicial decision making including those considering decisions rendered by judges in judicial hierarchy, whistle blowing, etc. One component which might meaningfully contribute to the extent literature is the rigorous consideration of the social and professional relationships between jurists and the impacts (if any) these relationships impose upon outcomes. Indeed, from a modeling standpoint, we believe the “judicial game” is a game on a graph–one where an individual strategic jurist must take stock of time evolving social topology upon which he or she is operating. Even among judges of equal institutional rank, we observe jurists with widely variant levels social authority (specifically social authority follows a power law distribution).

So what does all of this mean? Take whistle blowing — the power law distribution implies that if the average judge has a whistle, the “super-judges” we identify within the paper could be said to have an air horn. With the goal of enriching positive political theory / formal modeling of the courts, we believe the development of a positive theory of judicial social structure can enrich our understanding of the dynamics of prestige and influence. In addition, we believe, at least in part, “judicial peer effects” can help legal doctrine socially spread across the network. In that vein, here is a view of our operationalization of the social landscape … a wide shot of the broader network visualized using the Kamada-Kawai visualization algorithm:

Here is the current abstract for the paper: 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.