Coming Next Week on CLS Blog

A Netlogo 3D screenprint of one of the classic agent based models—the Shelling Segregation Model is above. We offer it as a holdover until CLS Blog Returns Sunday Night with more exciting content…..

NEXT WEEK:
(1) Discussion of a New Paper: Computer Programming and the Law
(2) Visualizing the 110th Congress — The House of Representatives
(3) For Law Students and Law Professors — Data on the Law Clerk Tournament
(4) And More …..

Data Mining the News — J. Kleinberg Work Discussed in MIT Tech Review

This short but cool article from MIT Technology Review discusses recent work by Computer Scientist Jon Kleinberg and his Cornell colleagues. This very nice visualization is the byproduct of their efforts at data mining more than 1 million online news items per day in the weeks leading up to the 2008 presidential election.

With Bankruptcy on Our Minds: The Structure of Title 11 U.S.C.

CLICK ON IT AND YOU CAN ZOOM IN and READ EVERY LABEL!

MOTIVATION:

We have become interesting in visualizing the structure of the law including its components and subcomponents.  In reduced form, statutes, regulations and certain other units of the law can be characterized in graph theoretical terms.  While we do not make deep inroads on the content of this above graph, we do generate a tree traversable visualization for its structure.

Much of my training in law school (particularly in the so called “code-based” classes) was focused upon developing mental models for the structure and content of graphs such as the one displayed above. In my case, I believe the usage of such a visualization early in a code-based course would have been beneficial. Thus, we offer this traversable visualization to the world for not only its research value but also for pedagogical purposes.

INSTRUCTIONS:

Start in the MIDDLE at the “11 U.S.C.__ ” Label and traverse out.

BREAKDOWN OF THE VISUAL:
GREEN NODE LABELS =   for SECTIONS  {In the Example below, 11 U.S.C. § 101}
YELLOW ARCS — Chapter 7 of Title 11 = LIQUIDATION
BLUE ARCS — Chapter 11 of Title 11 = REORGANIZATION  (aka “Filing Chapter 11“)
RED ARCS and GREY — Balance of the Chapters under Title 11
Red Arcs are for lines which lead to terminal nodes
Grey Arcs are for lines which do not immediately lead to terminal nodes

FINAL THOUGHTS:
Please feel free to PLAY AROUND and TEST IT OUT!
This is an early production version so please provide us with any feedback and/or suggestions.

Co-Sponsorship Networks– Senators of the 108th Congress

In the days and weeks to come we will turn our attenion away from Congress in favor of other institutions and substantive questions. However, given our prior posts focusing upon the structure of the 110th Congress, we thought it proper to highlight some relevant realted scholarship. James Fowler from the UCSD Political Science Department and leader of the Networks in Political Science movement has published several papers exploring the strucutre of legislative co-sponsorship.  You can find a link to these papers here. My favorite of these papers is Community Structure in Congressional Cosponsorship Networks published in Physica A by Yan Zhang, A. J. Friend, Amanda L. Traud, Mason A. Porter, James H. Fowler & Peter J. Mucha. The above figure, drawn from the paper, is a dendrogram for the legislative cosponsorship network of the Senate of the 108th Congress.

Senators of the 110th Congress Take 2-Contributions by Industry/Sector

This represents a deeper cut on campaign contributions to the Senators of 110th Congress. Again, we rely upon data from the Center for Responsive Politics.  The CRP aggregates contribution data up to the industry or economic sector. Thus, as before, we adopt their classification scheme and methodology herein.  While aggregating to the industry/sector level removes the degree of specificity we offered in our earlier post, it provides a cleaner representation for the graph.  For those interested in the other chamber, click here for the House of Representatives.

Click on the picture above and it will take you to our flash where you can zoom in and read the labels.

As you review the graph, please consider the following:

(1) Industries locate in the center of the graph because they provide significant funding to both Democrats and Republicans.

(2) Industries which generally only fund one political party are located toward the respective red/blue boundary.  For example, it is hardly surprising to observe the location of “Oil and Gas” relative to “Environmental” groups.

(3) It is important to note that we do not impose the partisan separation or the placement of party outliers apparent in the image. Rather, the algorithm places Red Senators in Blue Territory and Blue Senators in Red Territory because they receive significant sums from industries who typically fund the opposing party. For example, consider Senator Olympia J Snowe (R-ME) who is typically characterized as a moderate Republican.  Since she receives money from more industries that typically fund Democrats than Republicans, she is placed in Blue Territory by the algorithm.

(4) It is important not to over read the position of Senator Herb Kohl (D-WI).  Over the relevant time window, Senator Kohl received 94% of his resources through self-financing.

Google for Government? Broad Representations of Large N DataSets

In our previous post, a post which has generated tremendous interest from a variety of sources, we demonstrated how applying the tools of network science can provide a broad representation for thousands of lines of information.  Throughout the 2008 Presidential Campaign then Senator Obama consistently discussed his Google for Government initiative.

From the Obama for America Website:

Google for Government: Americans have the right to know how their tax dollars are spent, but that information has been hidden from public view for too long. That’s why Barack Obama and Senator Tom Coburn (R-OK) passed a law to create a Google-like search engine to allow regular people to approximately track federal grants, contracts, earmarks, and loans online.

We agree with both President Obama and Senator Coburn that universal accessibility of such information is worthwhile goal.  However, we believe this is only a first step.

In a deep sense, our prior post is designed to serve as a demonstration project.  We are just two graduate students working on a shoestring budget.  With the resources of the federal government, however, it would certainly be possible to create a series of simple interfaces designed to broadly represent of large amounts of information.  While these interfaces should rely upon the best available analytical methods, such methods could probably be built-in behind the scenes.   At a minimum, government agencies should follow the suggestion of David G. Robinson and his co-authors who argue the federal government “should require that federal websites themselves use the same open systems for accessing the underlying data as they make available to the public at large.”

Anyway, will be back on Monday providing more thoughts on our initial representation of the 110th Congress.  In addition, we hope to highlight other work in the growing field of Computational Legal Studies.  Have a good rest of the weekend!

Visualizing the Campaign Contributions to Senators in the 110th Congress — The TARP EDITION (The Image)

As part of our commitment to provide original content, we offer a Computational Legal Studies approach to the study of the current campaign finance environment.  If you click below you can zoom in and read the labels on the institutions and the senators.   The visualization memorializes contributions to the members of the 110th Congress (2007 -2009).  Highlighted in green are the primary recipients of the TARP.

In the post below, we offer detailed documentation of this visualization.

Three Important Principles: (1) Squares (i.e. Institutions) introduce money into the system and Circles (i.e. Senators) receive money  (2) Both Institutions and Senators are sized by dollars contributed or dollars received  (3) Senators are colored by Party.

Visualizing the Campaign Contributions to Senators in the 110th Congress — The TARP EDITION (Documentation for the Network)

Visualizing the Campaign Contributions to the Senators of the 110th Congress —
The TARP EDITION

By Michael Bommarito & Daniel Katz

University of Michigan
Center for the Study of Complex Systems
Department of Political Science

BASIC OVERVIEW:

110th Congress = January 3, 2007 – January 3, 2009

100 Members of the United States Senate

Click Here for the House of Representatives

BASIC RULE:

Squares (Institutions) Introduce Money into the System and Circles (Senators) Receive Money.

DATA OVERVIEW:

Using recently published data on campaign contributions collected by the Federal Election Commission and aggregated by the Center for Responsive Politics at http://www.opensecrets.org, our visualizations track large money donations to members of the 110th Congress over the 2003-2008 window.

Given that some senators resign or lose reelection, a subset of the senators of the 110th Congress have served less than the full 2003-2008 window. While this imposes some comparability issues, many of these new members faced challenging races and thus attracted significant sums of money.

It is important to note that most of these organizations did not directly donate. Rather, as noted by the Center for Responsive Politics “the money came from the organization’s PAC, its individual members or employees or owners, and those individuals’ immediate families. Organization totals include subsidiaries and affiliates. Of course, it is impossible to know either the economic interest that made each individual contribution possible or the motivation for each individual giver. However, the patterns of contributions provide critical information for voters, researchers and others.” The Center describes its methodology here http://www.opensecrets.org/politicians/method_pop.php.   We strike a tradeoff  between information overload and incomplete disclosure.  To provide for an optically tractable view of the top contributions, we impose the limiting requirement that to be included in our tally a given group’s contribution must fall within a given senator’s top contributor list.  For a first cut on the data, we believe this reaches an appropriate balance.  However, in subsequent work we plan to go much deeper and probe a much larger set of contribution information.

CONTRIBUTORS & CONTRIBUTIONS:

1,050 of the Donors are captured in the Graph.

Total Recorded Donations Introduced into the System by these Entities Total to  \$94, 138,917.

(1) SIZING of the SENATOR NODES — Each Circular node representing a U.S. Senator is sized according the amount of incoming donations. Thus, larger U.S. Senator nodes are the recipients of larger sums of money while the smaller nodes received smaller amounts of money.

NOTE ON SELF-FINANCING — Some candidates use personal funds to finance their campaigns. For example, Senator Herb Kohl (D-WI) spent \$5,922,759 of which \$5,575,000 (94%) came from his personal assets. In this respect, Senator Kohl has a significant “self-loop” but is sized very small because he accepts very little outside monies.

(2) COLORING of the SENATOR NODES — Each node representing a US Senator is colored according their Political Party. Using popular convention, we color members of the Republican Party as Red, members of the Democratic Party as Blue and Independents as Purple. For the 110th Congress, there are two Independents—Bernie Sanders (I- VT), Joe Lieberman (I- CT), respectfully.

(3) COLORING of the INSTITUTIONAL NODES — Each square node represents institutions who are top contributors to at least one Senator in the 110th Congress. The full graph contains 1,050 institutions of two separate classes. Green institutions are either primary TARP recipients or now components of primary recipients of resources under the Troubled Asset Relief Program. For example, we color Wachovia as Green even though they are now owned by Wells Fargo, a TARP recipient.

(4) SIZING of the INSTITUTIONAL NODES Each square node representing a TARP or Non-TARP institution is sized according their relative financial contribution to the over all system. Thus, larger institutions make larger contributions and smaller institutions make smaller contributions.

(5) SIZING of the CONNECTIONS Each Connection (Arc) between an Institution and a Senator is sized according to the amount of money flowing through a connection. Darker connections represent larger flows of money while lighter connections represent smaller amounts of money.

(6) COLORING of the CONNECTIONS — Each connection representing a campaign contribution from an institution to a US Senator is colored according to partisan affiliation of the receiving senator. Using popular convention, we color members of the Republican Party as Red, members of the Democratic Party as Blue and Independents as Purple. For the 110th Congress, there are two Independents—Bernie Sanders (I- VT), Joe Lieberman (I- CT), respectfully.

(7) STRUCTURE OF THE GRAPH The Graph is Visualized Using the Kamada-Kawai Visualization Algorithm.  This is an automated spring embedded, force directed placement algorithm often used in the network science literature to visualize graphs of this size.

(8) ACKNOWLEDGEMENTS  We thank Rick Riolo, Jon Zelner, Carl SimonScott Page and the Center for Responsive Politics for their comments, contributions and/or data.

Hustle & Flow: A Network Analysis of the American Federal Judiciary

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.

When is the first term enough?: On approximation in social science

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.

Print ‘Hello World’

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….

Computational Legal Studies

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!