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 ….
Here is another visual run through the SeaDragon Visualization from Microsoft Labs. Similar to the Title 17 United States Code visual from earlier in the week, one can zoom in and out. Using the button in the far southeast corner it is possible to generate a full screen visual of the network.
Pervious posts discussing this visualization are located here and here. In addition, results of our model of diffusion on the network are located here while an interactive version of the agent based model generated in Netlogo is located here. For those interested in the full draft … it is entitled Reproduction of Hierarchy? A Social Network Analysis of the American Law Professoriate.
Above are some graph statistics from our paper Hustle and Flow: A Social Network Analysis of the American Federal Judiciary. The paper measures the aggregated window of 1995-2005. These, of course, are not the only measures of centrality but they are commonly used in the network science literature. If you are interested — click through to the paper for a description of how these measures are calculated.
We believe these and other elements of the paper offer a good cut on the question of social prestige within the American Federal Judiciary. For example, in the paper we offer a graph visualization where Judge now Justice Alito as well as Judge turned Attorney General Michael Mukasey occupy an extremely central social position (just outside of the Top 25). We think this is useful because their social prestige is measured at a time period prior to their respective nominations. With appropriate control variables for the respective office in question … perhaps we might have been able to predict their nomination?
For purposes of the current Supreme Court opening and conditioned on the selection of a lower court justice with “blue team” credentials, we believe these graph statistics indicate Judge Sonia Sotomayor, Judge Merrick Garland , Judge William Fletcher & Judge David Tatel all occupy high levels of social prestige among their fellow judges and justices. Debate regarding qualifications of various individuals is likely to continue. While certainly not dispositive, we thought it appropriate to offer some social scientific evidence on the question.
President Obama might go outside the tradtional Federal Court of Appeals judge on this round. For example, Governor Jennifer Granholm, Solicitor General Elena Kagan and Homeland Security Secretary Janet Napolitano appear to be on the short list. However, keep these names in mind as this is probably not President Obama’s last nomination to the high court.
This is the final installment of posts related to Reproduction of Hierarchy? A Social Network Analysis of the American Law Professoriate. Thanks for your emails.
Here is the plot we provide within the paper. As a general proposition, we believe this represents an upper bound measure for the intellectual reach of an agenda offered by a given institution. With respect to our version of the Reed Frost Epidemiological Model, we use the p parameter to model “idea infectiousness.” When p = 1 every institution “contacted” by the idea is infected with the idea. When p = 0 no institution “contacted” by the idea is infected. In this version, we use the programming language python to run the model 500 times per institution. The above plot represents an estimate of the “diffusion curve” for each of the 184 institutions in our model. Building off central limit type properties, this leaves a far better estimate of reach than is offered in the single model run from the previous Netlogo GUI.
A cursory review of the above plot demonstrates, we are far from the land of linearity. Namely, a large number of institutions are able to reach much of the graph with very small changes in the value of p.
In the Structure of Scientific Revolutions, Kuhn quotes from Max Planck: “a new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.” Following Planck, we believe retirement is indeed be an important mechanism. However, we also argue the nature of the p parameter is a relevant consideration. In fact, unpacking various dimensions of p is the key to the broader model. Specifically, what are the properties of an idea that generate its infectiousness? Of course, we might like to believe infectiousness is related to a class of normatively attractive properties such as promoting efficiency or justice. However, it is not clear that this follows.
We took no pass on the question of whether some institutions would be better or worse at producing ideas with greater or lesser values of p. The motivated question for this post considers whether, in general, the institutions which are top producers of law professors are (1) leaders in innovation, (2) subsequent ratifiers of a newly established paradigm or (3) defenders of the status quo. In a deep sense, we are asking how to reasonably model decision making by the heterogeneous agents located at such institutions. Do institutions reward or punish intellectual risk-taking, search, etc.?
While this is an empirical question beyond the scope of this post, it worth asking because it partially informs the micro-dynamics plausibly responsible for generating the spread of new intellectual paradigms.
For the third installment of posts related to Reproduction of Hierarchy? A Social Network Analysis of the American Law Professoriate, we offer a Netlogo simulation of intellectual diffusion on the network we previously visualized. As noted in prior posts, we are interested legal socialization and its role in considering the spread of particular intellectual or doctrinal paradigms. This model captures a discrete run of the social epidemiological model we offer in the paper. As we noted within the paper, this represents a first cut on the question—where we favor parsimony over complexity. In the paper and in the model documentation, we offer some possible model extensions which could be considered in future scholarship.
Once you click through to the model, here is how it works:
(1) Click the Setup Button in the Upper Left Corner. This will Display the Network in the Circular Layout.
(2) Click the Layout Button. Depending upon the speed of your machine this may take up to 30 seconds. Stop the Layout Button by Re-Clicking the Button.
(3) Click the Size Nodes by Degree Button. You Will Notice the Fairly Central Node Colored in Red. This is School #12 Northwestern University Law School. Observe how we have set the default infected school as #12 Northwestern (Hat Tip to Uri Wilensky). A Full List of School Number is available at the bottom of the page when you click through.
(4) Now, we are ready to begin. Click the Spread Once Button. The idea then reaches its neighbors with probability p (set as a default at .05). You can click the Toggle Infection Tree button (at any point) to observe the discrete paths traversed by the idea.
(5) Click the Spread Once Button, again and again. Notice the plot tracking the time on the x axis and the number of institution infected on the y axis. This is an estimate of the diffusion curve for the institution.
(6) To restart the simulation, click the Reinfect One button. Prior to hitting this button, slide the Infected Slider to any Law School you would like to observe. Also, feel free to adjust the p slider to increase or decrease the infectiousness of the idea.
Please comment if you have any difficulty or questions. Note you must have Java 1.4.1 + installed on your computer. The Information Technology professionals at many institutions will have already installed this on your machine but if not you will need to download it. We hope you enjoy!
This is the second post related to our recently released paper Reproduction of Hierarchy? A Social Network Analysis of the American Law Professoriate. We believe a hierarchical depiction of the network helps uncover latent distribution of authority present in the law professor network. Specifically, between 10-15 law schools are responsible socializing a significant percentage of the future legal academics.
This extreme skewing of social authority presented in the visualization is also displayed in the above log-log of the degree distribution. While the -1.93 alpha level and size of the network falls outside the traditional power law/scale free range, the plot above does demonstrate the extreme skewing of social authority among institutions of legal education.
This is the time of year when there is significant discussion of the US News Rankings. We believe there is a wide-class of available data—data which could be characterized as the revealed preferences of important actors within the American legal system. For example, we have previous offered information on how judges select clerks from law schools? Now, we offer some empirical insight into how hiring committees vote over various institutions?
Coming later in the week, we will offer a Netlogo GUI which allows a user to start ideas at various institutions and observe how many generations they take to spread from institution to institution. There are significant caveats in the interpretation of our model but we think many people will still find it interesting. Please stay tuned and as always please tell your friends and colleagues about the CLS Blog!
Last month, my colleagues and I posted Reproduction of Hierarchy? A Social Network Analysis of the American Law Professoriate to the SSRN. We have been quite happy with the initial response to the article. It has been posted on a number of blogs including ELS Blog, Tax Prof Blog, Legal Theory Blog, US News and World Report Blog as well as several others.
To develop the network, our paper uses an approach first developed in the network science literature by Fowler, Grofman & Masuoka. The network visualized above relies upon mapping the institution where an individual received their primary legal socialization to the institution where that individual acts to socialize the next generation. The example below demonstrates the move from our data set to the full graph of 184 Institutions and 7,200+ Professors. In the full graph, the institutions are sized by the number of inbound connections. Harvard Law School has the largest number of placements and is the largest node contained in the graph.
This is first of a multipart series of posts regarding the ideas contained in the paper. In subsequent posts, I hope to comment on a variety of matters including how we use the above network to computationally model spread of intellectual and doctrinal paradigms. To preview, we use the above network architecture in a Reed-Frost style social epidemiological model.
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.