Courts of last resort in the United States are becoming increasingly important in American politics as the number of cases, influential decisions, and controversial issues continue to rise in the states. In discussions of federalism in the United States, these critical institutions are often overlooked as a complex system, due to substantial data limitations on the behavior and outcomes of these courts. I situate state courts of last resort as a complex adaptive system in the broader U.S. framework. I then seek to redress the data shortcomings by introducing a comprehensive database on state courts of last resort from 1953-2010. Using advanced data capture techniques, I evaluate my parsers to capture the ever-changing structures of the source documents. This database will be the largest in scope and case detail to date. Moreover, it should further our understanding of judicial decision making and assist the prediction of the impact of institutional change on the system. In addition, I modeled and analyzed the system as a complex adaptive system. Since the system has network characteristics, I used the approach of network science to model the system based on the citation behavior. Moreover, I created an automated dictionary-based classification model to extract and classify the citation treatments for the court cases. Using state-of-the-art algorithms in network science and natural language processing, I was able to analyze the system and test the performance of the algorithms based on the system characteristics.