Antiquated technology plagues the legal industry. While the legal market generates billions of dollars of revenue annually, the technology that supports it is decentralized, inconsistent and outdated. With the purpose of promoting modernization and illustrating how technology could improve existing inefficiencies in legal research, this thesis evaluates the creation and execution of an automated lawsuit classification system called the Civil Lawsuit Analysis System ("CLAS"). CLAS classifies the nature of a lawsuit based on information contained in a subset of attorney-authored court documents called "Memorandum of Points and Authorities" that are filed with trial courts during the process of the lawsuit. In light of the fact that no similar automated classification system exists today, developing the process by which CLAS operates proved to be a collaborative and time-intensive process that ultimately required specialized knowledge in both the computer science and the legal fields.

Human communication has features, such as syntax, unseen in any other form of animal communication. How did we come to use such a sophisticated form of communication? This paper addresses the issue of the origin of compositionality in languages using animat-based modeling. In the simulation, 100 software agents controlled by neural networks are given the ability to communicate with each other using sequences of signals. These agents---called animats---play communication games with each other, and if they are among the most effective communicators, they mate and produce offspring. Through evolution and learning, animats developed languages that exhibit some rudimentary compositionality in simple environment settings, but under more sophisticated environments, they struggled to establish effective protocols of communication. Results suggested that simple recurrent neural networks allowed animats to develop languages with rudimentary compositionality, and that imitation learning helped animats come to consensus on usage of signals.