Measuring the Complexity of the Law: The United States Code (By Daniel Martin Katz & Michael J. Bommarito)

From our 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, 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.”

This is a draft version so we invite your comments ( and (  Also, for those who might be interested – we are building out a full replication page for the paper.  In the meantime, all of the relevant code and data can be accessed at GitHub and from the Cornell Legal Information Institute.

UPDATE: Paper was named “Download of the Week” by Legal Theory Blog.

You Had Me at Hello: How Phrasing Affects Memorability [via]

From the Abstract: “Understanding the ways in which information achieves widespread public awareness is a research question of significant interest. We consider whether, and how, the way in which the information is phrased — the choice of words and sentence structure — can affect this process. To this end, we develop an analysis framework and build a corpus of movie quotes, annotated with memorability information, in which we are able to control for both the speaker and the setting of the quotes. We find significant differences between memorable and non-memorable quotes in several key dimensions. One is lexical distinctiveness: in aggregate, memorable quotes use less common word choices, but at the same time are built upon a scaffolding of common syntactic patterns; another is that memorable quotes tend to be more general in ways that make them easy to apply in new contexts. We also show how the concept of “memorable language” can be extended across domains.”

Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network [ via Peter Erdi and UMich EECS ]

Dr. Peter Erdi is one of the leading scholars studying the path of innovation as revealed through the U.S. Patent Citation network.  Above is his most recent talk – Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network

For some of his additional work please see:
Patent Citation Networks Revisited: Signs of a Twenty-First Century Change?
, North Carolina Law Review (2009).
Modeling Innovation by a Kinetic Description of the Patent Citation System
, Physica A (2007)
Law and the Science of Networks: An Overview and an Application to the ‘Patent Explosion’
, Berkeley Technology Law Journal (2007).

[Note: Load Time for the Video above is a bit slow]

Model Thinking – A Free Online Course with Scott E. Page (Director of UMich Center for Study of Complex Systems)

Starting in the January 2012, Scott E. Page (one of my PhD thesis advisors) will teach Model Thinking (a free online course offered via the consortium that brought you AI Class, Machine Learning, etc.)

Scott and I have previously teamed up to teach Complex Systems @ the ICPSR Summer Methods Program (where I teach the model implementation lab).  Over 7,000 people and counting have are already signed up …