Measuring, Monitoring, and Managing Legal Complexity

Author:J.B. Ruhl & Daniel Martin Katz
Position:David Daniels Allen Distinguished Chair of Law, Vanderbilt University Law School/Associate Professor of Law
Pages:191-244
SUMMARY

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... (see full summary)

 
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191
Measuring, Monitoring, and Managing
Legal Complexity
J.B. Ruhl & Daniel Martin Katz
ABSTRACT: 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
David Daniels Allen Distinguished Chair of Law, Vanderbilt University Law School.
 Associate Professor of Law, Chicago-Kent College of Law; External Affiliated Faculty,
CodeX: The Stanford Center for Legal Informatics.
We are thankful for comments from participants in workshops conducted by Lancaster
University, the University of San Diego’s Center for Computation, Mathemat ics, and Law; the
Society for Evolutionary Analysis in Law; and the German Law and Society Association. We also
are grateful to our respective institutions for research support. Please direct comments or
questions to jb.ruhl@vanderbilt.com and dkatz3@kentlaw.iit.edu.
192 IOWA LAW REVIEW [Vol. 101:191
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.
I. INTRODUCTION ............................................................................. 193
II. THE COMPLEXITY SCIENCE THEORY OF LEGAL COMPLEXITY ....... 201
A. DESCRIPTIVE THEORIES ........................................................... 203
B. PRESCRIPTIVE THEORIES .......................................................... 207
C. ETHICAL THEORIES ................................................................. 210
III. MEASURING LEGAL COMPLEXITY .................................................. 211
A. COMPLEXITY AND SYSTEM STRUCTURE ..................................... 212
1. Agents and Agent Sets: Composition, Classification, and
Diversity .......................................................................... 212
2. Formal Architecture: Trees and Other Formal
Hierarchies .................................................................... 213
3. Network Architecture: Emergent Hierarchies ............ 216
4. Information Storage and Computation ....................... 222
B. COMPLEXITY AND SYSTEM BEHAVIOR ....................................... 224
1. Networks, Trees, Diffusion, and System Behavior ...... 226
2. Emergence, Feedback, Laplace’s Demon, and System
Prediction ...................................................................... 228
i. Feedback .................................................................... 229
ii. Emergence .................................................................. 229
IV. MONITORING LEGAL COMPLEXITY ............................................... 231
A. DESIGNING “LEGAL MAPS FOR NETWORK BEHAVIOR
MONITORING .......................................................................... 232
B. TESTING SYSTEM PERFORMANCE .............................................. 234
1. Synchronization Monitoring ........................................ 234
2. Stress Tests ..................................................................... 237
3. Interdependent Systems Analysis ................................. 237
C. COMPARATIVE DESIGN STUDIES ................................................ 238
V. MANAGING LEGAL COMPLEXITY ................................................... 238
A. DEFINING TOO MUCH COMPLEXITY ......................................... 238
2015] LEGAL COMPLEXITY 193
B. MANAGING TOO MUCH COMPLEXITY ....................................... 240
1. Real-Time Monitoring Leveraging Learning
Algorithms ..................................................................... 240
2. Designing Legal User Interfaces .................................. 242
VI. CONCLUSION ................................................................................ 243
I. INTRODUCTION
Do you believe the U.S. Tax Code is too complex? If so, you are in good
company—most Americans believe the Tax Code is too complex.1 Many legal
scholars believe the Tax Code is too complex.2 Even the Internal Revenue
Service’s own National Taxpayer Advocate Service believes the Tax Code is
too complex.3 And this is not a new sentiment in society—“[s]ince the
inception of the federal income tax, commentators have viewed complexity
as virtually inevitable.”4 But prove it! That’s right, prove the Tax Code is too
complex.
How would you do that? How would you prove the Tax Code—or any
other statute or regulation—is too complex? Making such a claim about some
element of law requires proof of two related hypotheses. The first is
descriptive: the law is complex. The second is normative: the law is too complex.
Both are, in theory, empirically testable hypotheses. The claim that a body of
law is complex requires some convention for defining legal complexity and
1. Surveys routinely find the vast majority of American taxpayers believe the Code is too
complex. See, e.g., Courtney Coren, Tax Poll: Most Americans Say Tax Code Is Too Complicated,
NEWSMAX (Apr. 11, 2013, 9:27 AM), http://www.newsmax.com/Newsfront/quinnipiac-poll-irs-
tax/2013/04/11/id/498974. Google searches for “U.S. tax code too complex” and “tax code is
too complex” yield millions of sites.
2. For a small slice, see Michelle Arnopol Cecil, Toward Adding Further Complexity to the
Internal Revenue Code: A New Paradigm for the Deductibility of Capital Losses, 99 U. ILL. L. REV. 1083
(1999); Steven A. Dean, Attractive Complexity: Tax Deregulation, the Check-the-Box Election, and the
Future of Tax Simplification, 34 HOFSTRA L. REV. 405 (2005); Samuel A. Donaldson, The Easy Case
Against Tax Simplification, 22 VA. TAX REV. 645 (2003); James S. Eustice, Tax Complexity and the
Tax Practitioner, 45 TAX L. REV. 7 (1989); Stanley A. Koppelman, At-Risk and Passive Activity
Limitations: Can Complexity Be Reduced?, 45 TAX L. REV. 97 (1989); Susan B. Long & Judyth A.
Swingen, An Approach to the Meas urement of Tax Law Complexity, 8 J. AM. TAX ASSN 22 (1987);
Edward J. McCaffery, The Holy Grail of Tax Simplification, 1990 WIS. L. REV. 1267; John A. Miller,
Indeterminacy, Complexity, and Fairness: Justifying Rule Simplification in the Law of T axation, 68 WASH.
L. REV. 1 (1993); and Deborah L. Paul, The Sources of Tax Complexity: How Much Simplicity Can
Fundamental Tax Reform Achieve?, 76 N.C. L. REV. 151 (1997).
3. 1 TAXPAYER ADVOCATE SERV., INTERNAL REVENUE SERV., NATIONAL TAXPAYER ADVOCATE
2012 ANNUAL REPORT TO CONGRESS 3 (2012), http://www.taxpayeradvocate.irs.gov/2012-Annual-
Report/downloads/Most-Serious-Problems-Tax-Code-Complexity.pdf (“The most serious problem
facing taxpayers—and the IRS—is the complexity of the Internal Revenue Code (the ‘ta x code’).”).
4. Eric Kades, The Laws of Complexity and the Complexity of Laws: The Implications of
Computational Complexity Theory for the Law, 49 RUTGERS L. REV. 403, 408 (1997) (citing
commentary from the early 1900s).

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