The American legal system is often accused of being “too complex.” For example, most Americans believe the Tax Code is too complex. But what does that mean, and how would one prove the Tax Code is too complex? Both the descriptive claim that an element of law is complex and the normative claim that it is too complex should be empirically testable hypotheses. Yet, in fact, very little is known about how to measure legal complexity, much less how to monitor and manage it.
Legal scholars have begun to employ the science of complex adaptive systems, also known as complexity science, to probe these kinds of descriptive and normative questions about the legal system. This body of work has focused primarily on developing theories of legal complexity and positing reasons for, and ways of, managing it. Legal scholars thus have skipped the hard part—developing quantitative metrics and methods for measuring and monitoring law’s complexity. But the theory of legal complexity will remain stuck in theory until it moves to the empirical phase of study. Thinking about ways of managing legal complexity is pointless if there is no yardstick for deciding how complex the law should be. In short, the theory of legal complexity cannot be put to work without more robust empirical tools for identifying and tracking complexity in legal systems.
This Article explores legal complexity at a depth not previously undertaken in legal scholarship. First, the Article orients the discussion by briefly reviewing complexity science scholarship to develop descriptive, prescriptive, and ethical theories of legal complexity. The Article then shifts to the empirical front, identifying potentially useful metrics and methods for studying legal complexity. It draws from complexity science to develop methods that have been or might be applied to measure different features of legal complexity. Next, the Article proposes methods for monitoring legal complexity over time, in particular by conceptualizing what we call Legal Maps—a multi-layered, active representation of the legal system network at work. Finally, the Article concludes with a preliminary examination of how the measurement and monitoring techniques could inform interventions designed to manage legal complexity by using currently available machine learning and user interface design technologies.
Daniel Katz & J.B. Ruhl,
Measuring, Monitoring and Managing Legal Complexity,
Iowa L. Rev.
Available at: http://scholarship.kentlaw.iit.edu/fac_schol/865