Measuring the complexity of the law: the United States Code

Abstract

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, most 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 begin to 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. We measure various features of a title including its structural size, the net flow of its intra-title citations and its linguistic entropy. 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.

Measuring and Modeling the U.S. Regulatory Ecosystem

“…In future work, we intend to expand upon these questions and connect to an extant research agenda, including the development of a more detailed theoretical framework, the modeling of agent-based or computational regulatory ecosystems, the categorization of regulatory "species" and "climates," and the integration of this analysis with our existing work on the complexity of other statutory, regulatory, and judicial systems [22,23].…”

Legal entity recognition in an agglutinating language and document connection network for EU Legislation and EU/Hungarian Case Law

“…Network graphs have been increasingly used to explore complex relationships among data. More relevant to our present subject, they have been used to study U.S. legislation complexity [8] and legal precedent evolution [9].…”

Ex Machina: Analytical platforms, Law and the Challenges of Computational Legal Science

“…The area, today often referred to as Computational legal science, is the most recent development of this process, a development taking place in a historical moment in which the perimeter of the research areas potentially interesting for the jurist has extended to include complexity sciences [109][110][111], computational social science [112] along with a considerable number of computational techniques emerged in the most disparate disciplinary fields.…”