Network Analysis and the Law: Measuring the Legal Importance of Precedents at the U.S. Supreme Court by James H. Fowler, et al.
Abstract:
We construct the complete network of 26,681 majority opinions written by the U.S. Supreme Court and the cases that cite them from 1791 to 2005. We describe a method for using the patterns in citations within and across cases to create importance scores that identify the most legally relevant precedents in the network of Supreme Court law at any given point in time. Our measures are superior to existing network-based alternatives and, for example, offer information regarding case importance not evident in simple citation counts. We also demonstrate the validity of our measures by showing that they are strongly correlated with the future citation behavior of state courts, the U.S. Courts of Appeals, and the U.S. Supreme Court. In so doing, we show that network analysis is a viable way of measuring how central a case is to law at the Court and suggest that it can be used to measure other legal concepts.
Danny Bickson pointed this paper out in: Spotlight: Ravel Law – introducing graph analytics to law research.
Interesting paper but remember that models are just that, models. Subsets of a more complex reality.
For example, I don’t know of any models of the Supreme Court (U.S.) that claim to be able to predict The switch in time that saved nine. If you don’t know the story, it makes really interesting reading. I won’t spoil the surprise but you will come away feeling the law is less “fixed” than you may have otherwise thought.
I commend this paper to you but if you need of legal advice, it’s best to consult an attorney and not an model.