Earlier in the month, there was a very interesting discussion over at Flowing Data entitled the Rise of the Data Scientist. We decided to highlight it in this post because it raises important issues regarding the relationship between Computational Legal Studies and other movements within law.
As we consider ourselves empiricists, we are strong supporters of the Empirical Legal Studies movement. For those not familiar, the vast majority of existing Empirical Legal studies employ the use of econometric techniques. For some substantive questions, these approaches are perfectly appropriate. While for others, we believe techniques such as network analysis, computational linguistics, etc. are better suited. Even when appropriately employed, as displayed above, we believe the use of traditional statistical approaches should be seen as nested within a larger process. Namely, for a certain class of substantive questions, there exists tremendous amounts of readily available data. Thus, on the front end, the use of computer science techniques such as web scraping and text parsing could help unlock existing large-N data sources thereby improving the quality of inferences collectively produced. On the back end, the use of various methods of information visualization could democratize the scholarship by making the key insights available to a much wider audience.
It is worth noting that our commitment to Computational Legal Studies actually embraces a second important prong. From a mathematical modeling/formal theory perspective, at least for a certain range of questions, agent based models/computational models ≥ closed form analytical models. In other words, we are concerned that many paper & pencil game theoretic models fail to incorporate interactions between components or the underlying heterogeneity of agents. Alternatively, they demonstrate the existence of a P* without concern of whether such an equilibrium is obtained on a timescale of interest. In some instances, these complications do not necessarily matter but in other cases they are deeply consequential.