22 Artificial Intelligence and Law 337 (2014)
41 Pages Posted: 8 Aug 2013 Last revised: 8 Feb 2016
Date Written: August 1, 2013
Einstein’s razor, a corollary of Ockham’s razor, is often paraphrased as follows: make everything as simple as possible, but not simpler. This rule of thumb describes the challenge that designers of a legal system face — to craft simple laws that produce desired ends, but not to pursue simplicity so far as to undermine those ends. Complexity, simplicity’s inverse, taxes cognition and increases the likelihood of suboptimal decisions. In addition, unnecessary legal complexity can drive a misallocation of human capital toward comprehending and complying with legal rules and away from other productive ends.
While many scholars have offered descriptive accounts or theoretical models of legal complexity, empirical research to date has been limited to simple measures of size, such as the number of pages in a bill. No extant research rigorously applies a meaningful model to real data. As a consequence, we have no reliable means to determine whether a new bill, regulation, order, or precedent substantially effects legal complexity.
In this paper, we address this need by developing a proposed empirical framework for measuring relative legal complexity. This framework is based on “knowledge acquisition,” an approach at the intersection of psychology and computer science, which can take into account the structure, language, and interdependence of law. We then demonstrate the descriptive value of this framework by applying it to the U.S. Code’s Titles, scoring and ranking them by their relative complexity. Our framework is flexible, intuitive, and transparent, and we offer this approach as a first step in developing a practical methodology for assessing legal complexity.
Keywords: United States Code, Complexity of Law, Legal Complexity, legal entropy, optimal regulation
Suggested Citation: Suggested Citation
Katz, Daniel Martin and Bommarito, Michael James, Measuring the Complexity of the Law: The United States Code (August 1, 2013). 22 Artificial Intelligence and Law 337 (2014) . Available at SSRN: https://ssrn.com/abstract=2307352 or http://dx.doi.org/10.2139/ssrn.2307352