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Measuring the complexity of the law: the United States Code

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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.

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Notes

  1. Recent evidence points to a potential bipartisan political constituency in favor of at least basic overtures toward simplicity. H.R. 946: Plain Writing Act of 2010 (Signed by President Obama on October 13, 2010) is designed “[T]o enhance citizen access to Government information and services by establishing that Government documents issued to the public must be written clearly, and for other purposes”.

  2. This data set was provided by the Cornell Legal Information Institute and can be accessed at http://hula.law.cornell.edu/uscode_xml_dist/usc-xml-2010-10-28/. The United States Code features a total of fifty Titles. However, Title 34—Navy has been repealed. With the recent approval of Title 51—National and Commercial Space Programs the United States Code will once again feature a total of fifty active Titles. All code and additional replication materials are available here https://github.com/mjbommar/us-code-complexity.

  3. End users of the Code are actors who interact directly with its text. End users include not only sophisticated parties, such as lawyers and lawmakers, but also laypersons, public interest groups, and businesses.

  4. A “slip law” is the first print of a new law in pamphlet form, usually available 2–3 days after enactment. The Government Printing Office (GPO) offers a useful description of this process see http://www.gpoaccess.gov/plaws/about.html.

  5. While we do not incorporate these regulations into our analysis, we recognize that their incorporation would paint a more complete picture of the relevant legal landscape. Through a process similar to the compilation of the United States Code, federal regulations are subsequently compiled by topic in the Code of Federal Regulations (C.F.R.). Of course, administrative regulations and the United States Code are not the only sources of federal legal materials. There also exist additional materials such as judicial decisions, executive orders, revenue rulings, etc.

  6. As Title 9 is the smallest Title in the United States Code, it allows us to clearly indicate these distinctions that would otherwise be obscured by the size of the tree for other Titles.

  7. While Chapter 1 is explicitly labeled, the remaining Chapters are located at the same horizontal level of the hierarchy.

  8. Since Tn is a tree as in Fig. 1, As must be |V| − 1.

  9. All code and additional replication materials are available here https://github.com/mjbommar/us-code-complexity.

  10. The online appendix can be access here: http://computationallegalstudies.com/measuring-legal-complexity-appendix/.

  11. One obvious weakness with our proposed measure of linguistic complexity is its failure to capture the underlying semantics. As this is an introductory effort, we would invite future work focused upon this particular dimension of the question.

  12. We acknowledge the extensive literature on text complexity (e.g., Flesch and Gould 1949; Kincaid et al. 1975; Si and Callan 2001). Much of this work, however, is directed at reading comprehension of standard sentences and paragraphs. The United States Code is a specialized document with its passages separated by the unique presentation formatting used to display statutes.

  13. We offer this as a ceteris paribus proposition across the millions of words contained in the United States code.

  14. We selected tokens rather than other alternative length measures, such as pages, as we believe these are far less likely to be impacted by formatting conventions.

  15. There exist additional potential complications. For example, in some instances, longer words are more specific and thus their use can result in less ambiguity.

  16. It should be acknowledged that compression ratios are a common alternative to entropy measures. However, due to the large variation in compression algorithms and their implementation-specific behaviors, we felt that simple Shannon entropy was a more reproducible measure than compression.

  17. This is the red, green, blue or RGB value. A pure black canvas has an RGB value = #000000.

  18. In the random signal case, each pixel is assigned a random color assignment. The pseudocode for this assignment requires a randomly generated string of numbers where the assigned number corresponds to an RGB value and the length of the string is equal to the number of pixels on the canvas.

  19. In expectation, given an initial random assignment of pixel colors and a reasonably large canvas, there is likely to be at least some clustering of RGB values. This implies that at least some form of reduced representation is possible. However, this compression will be nominal.

  20. In the context of message compression, the fragment “orange in of going the not large kick more end to …” does not easily lend itself to reduced form representation.

  21. In the case of the uniform signal, the first fragment “dog” is the only new information content that is imparted to the end user. With only the first fragment and the total length of the message the signal could be quickly compressed.

  22. While a number of alternative and more sophisticated forms of entropy exist, the original Shannon entropy measure is the most straight-forward measure and is still commonly used in the information science literature. Thus, for the purpose of comparing the distribution of words within Titles, we apply the Shannon entropy. For additional work on entropy see Tsallis (1988) and Rényi ( 1961 ).

  23. Following upon common practice in the field of information retrieval and computational linguistics, we use the stopword list from the Natural Language Toolkit (NLTK) available at http://www.nltk.org/.

  24. There has been a significant amount of recent work on statutory citations including but not limited to Bourcier and Mazzega (2007b); Bommarito and Katz (2010); Boulet et al. (2011); Mazzega et al. (2011).

  25. Of course, if an element contains no citations whatsoever, then the protocol above collapses to only the first two rules. However, given many elements of the Code do contain citations, we embed this consideration into our analysis.

  26. This “walk” is by no means a random walk. Rather, it could better be described as a special case of graph traversal. These extended citation paths can grow to be quite lengthy. The maximum path length from 46 USC §51510 and 7 USC §87e requires thirty-two separate steps to complete.

  27. As an additional complication, note that when a named Act like the IRC of 1986 is cited, one must consult a short name list in order to determine where the Act was codified.

  28. Instead, the citation graph disobeys the hierarchical or vertical tree and memorializes various horizontal connections between elements.

  29. In the strongly connected component of the graph, there is a directed path from each vertex in the graph to every other vertex. In the weakly connected component, there is an undirected path from each vertex in the graph to every other vertex.

  30. A given section can feature internal references to other internal provisions. For purposes of this measurement, we do not distinguish this case from the more general case of interdependence.

  31. It is really important to highlight the wide set of potential composite complexity measures that one could contemplate. The purpose of this article is to set forth some of the core components that might be contemplated in a future application.

  32. The online appendix can be access here: http://computationallegalstudies.com/measuring-legal-complexity-appendix/.

  33. In this case, this is akin to assigning each measure a weight of \(\frac{1}{3}\).

  34. While mere averaging has a certain attraction, it also represents a somewhat arbitrary approach. Given that we do not have any specific theoretical grounds that justify a departure, we have chosen this naïve approach.

  35. In this case, “normalization” implies that in all components that comprise the composite measure the size of the Title is controlled for in one respect or another. Therefore, the measured highlighted Table 12 all measures feature a “per section” or some other analogous form of standardization.

  36. Again, these are structure, interdependence and language.

  37. The online appendix can be access here: http://computationallegalstudies.com/measuring-legal-complexity-appendix/.

  38. The online appendix can be access here: http://computationallegalstudies.com/measuring-legal-complexity-appendix/.

References

  • Achen CH (1978) Measuring representation. Am J Polit Sci 22:475–510

  • Ansolabehere S, Snyder JM Jr, & Stewart C III (2001) Candidate positioning in US House elections. Am J Polit Sci 45:136–159

  • Arrow KJ (1963) Social choice and individual values. Yale University Press, New Haven

    Google Scholar 

  • Austen-Smith D, Banks JS (1996) Information aggregation, rationality, and the Condorcet jury theorem. Am Polit Sci Rev 90:34–45

  • Barton BH (2008) Judges, lawyers, and a predictive theory of legal complexity. University of Tennessee Legal Studies Research Paper No. 31

  • Bates JE, Shepard HK (1993) Measuring complexity using information fluctuation. Phys Lett A 172(6):416–425

    Article  Google Scholar 

  • Becker GS (1983) A theory of competition among pressure groups for political influence. Q J Econ 98(3):371–400

    Article  Google Scholar 

  • Bibel LW (2004) AI and the conquest of complexity in law. Artif Intell Law 12(3):159–180

    Article  MathSciNet  Google Scholar 

  • Bittker BI (1974) Tax reform and tax simplification. U Miami L Rev 29:1

    Google Scholar 

  • Black D (1948) On the rationale of group decision-making. J Polit Econ 56(1):23

    Article  Google Scholar 

  • Bommarito MJ II, Katz DM (2010) A mathematical approach to the study of the United States code. Physics A 389(19):4195–4200

    Article  Google Scholar 

  • Bommarito II, Michael J, Katz DM (2009) Properties of the United States code citation network. arXiv preprint arXiv:0911.1751

  • Bonanno C, Collet P (2007) Complexity for extended dynamical systems. Commun Math Phys 275(3):721–748

    Article  MATH  MathSciNet  Google Scholar 

  • Boose JH (1989) A survey of knowledge acquisition techniques and tools. Knowl Acquis 1(1):3–37

    Article  Google Scholar 

  • Boose JH, Gaines BR (1990) The foundation of knowledge acquisition. Academic Press Professional, San Diego

  • Bose R (2002) Information theory, coding and cryptography. Tata McGraw-Hill Education

  • Boulet R, Mazzega P, Bourcier D (2011) A network approach to the French system of legal codes—part I: analysis of a dense network. Artif Intell Law 19(4):333–355

    Article  Google Scholar 

  • Bourcier D, Mazzega P (2007) Toward measures of complexity in legal systems. In: Proceedings of the 11th international conference on artificial intelligence and law. ACM, pp 211–215

  • Bourcier D, Mazzega P (2007b) Codification law article and graphs. In: Lodder AR, Mommers L (eds) Legal knowledge and information systems. IOS Press, pp 29–38; ISBN 978-1-58603-810-6

  • Buchanan JM, Tullock G (1965) The calculus of consent: logical foundations of constitutional democracy, vol 100. University of Michigan Press, Ann Arbor

    Google Scholar 

  • Buckley JJ (1984) The multiple judge, multiple criteria ranking problem: a fuzzy set approach. Fuzzy Sets Syst 13(1):25–37

    Article  MATH  Google Scholar 

  • Cecil MA (1999) Toward adding further complexity to the internal revenue code: a new paradigm for the deductibility of capital losses. U Ill L Rev 1083–1139

  • Cimiano P, Hotho A, Staab S (2005) Learning concept hierarchies from text corpora using formal concept analysis. J Artif Intell Res 24:305–339

    MATH  Google Scholar 

  • Cox GW, McCubbins MD (2007) Legislative leviathan: party government in the House. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Csiszar I (1991) Why least squares and maximum entropy? An axiomatic approach to inference for linear inverse problems. Ann Stat 19(4):2032–2066

    Article  MATH  MathSciNet  Google Scholar 

  • Donaldson SA (2003) Easy case against tax simplification. Va Tax Rev 22:645

    Google Scholar 

  • Downs A (1957) An economic theory of democracy. Harper & Brothers, New York

  • Dworkin R (1986) Law’s empire. Harvard University Press, Cambridge

    Google Scholar 

  • Eckenrode RT (1965) Weighting multiple criteria. Manag Sci 12(3):180–192

    Article  Google Scholar 

  • Einstein A (1934) On the method of theoretical physics. Philos Sci 1(2):163–169

    Article  Google Scholar 

  • Epstein RA (1995) Simple rules for a complex world. Harvard University Press, Cambridge

    Google Scholar 

  • Epstein RA (2004) The optimal complexity of legal rules. Law School, University of Chicago. Olin Working Paper No. 210

  • Eustice JS (1989) Tax complexity and the tax practitioner. Tax L Rev 45:7

    Google Scholar 

  • Fainmesser I, Fershtman C, Gandal N, Panunzi F (2005) A consistent weighted ranking scheme with an application to NCAA college football rankings. Centre for Economic Policy Research

  • Feldman DP, Crutchfield JP (1998) Measures of statistical complexity: why? Phys Lett A 238(4):244–252

    Article  MATH  MathSciNet  Google Scholar 

  • Feltovich PJ, Spiro RJ, Coulson RL, Myers-Kelson A (1995) Reductive bias and the crisis of text (in the law). J Contemp Legal Issues 6:187

    Google Scholar 

  • Ferstl EC, von Cramon DY (2007) Time, space and emotion: fMRI reveals content-specific activation during text comprehension. Neurosci Lett 427(3):159–164

    Article  Google Scholar 

  • Flesch R, Gould AJ (1949) The art of readable writing. Harper, New York, p 196

  • Flournoy A (1994) Coping with complexity. Loyola of Los Angeles Law Rev 27(3):809

  • Francesconi E (2011) A learning approach for knowledge acquisition in the legal domain. In: Sartor G, Casanovas P, Biasiotti M, Fernández-Barrera M (eds) Approaches to legal ontologies. Springer, Netherlands, pp 219–233

  • Frisch D (2011) Commercial law’s complexity. Geo Mason L Rev 18:245

    Google Scholar 

  • Ganapathi V, Vickrey D, Duchi J, Koller D (2012) Constrained approximate maximum entropy learning of markov random fields. arXiv preprint arXiv:1206.3257

  • Gibbard A (1973) Manipulation of voting schemes: a general result. Econometrica 41(4):587–601

  • Golan A, Judge G, Perloff J (1997) Estimation and inference with censored and ordered multinomial response data. J Econom 79(1):23–51

    Article  MATH  MathSciNet  Google Scholar 

  • Halford GS, Busby J (2007) Acquisition of structured knowledge without instruction: the relational schema induction paradigm. J Exp Psychol Learn Mem Cogn 33(3):586

    Article  Google Scholar 

  • Hamming RW (1986) Coding and information theory. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  • Harsanyi JC (1955) Cardinal welfare, individualistic ethics, and interpersonal comparisons of utility. J Polit Econ 63:309–321

    Article  Google Scholar 

  • Holsapple CW, Raj V, Wagner WP (2008) An experimental investigation of the impact of domain complexity on knowledge acquisition (KA) methods. Expert Syst Appl 35(3):1084–1094

    Article  Google Scholar 

  • Iria J (2009) A core ontology of knowledge acquisition. In: Aroyo L, Traverso P, Ciravegna F, Cimiano P, Heath T, Hyvönen E, Mizoguchi R, Oren E, Sabou M, Simperl E (eds) The semantic web: research and applications. Springer, Berlin, pp 233–247

  • Jaynes ET (1957) Information theory and statistical mechanics. Phys Rev 106(4):620

    Article  MATH  MathSciNet  Google Scholar 

  • Kades E (1997) Laws of complexity and the complexity of laws: the implications of computational complexity theory for the law. Rutgers L Rev 49:403

    Google Scholar 

  • Kaplow L (1995) A model of the optimal complexity of legal rules. J Law Econ Organ 11:150

  • Kincaid JP, Fishburne RP Jr, Rogers RL, Chissom BS (1975) Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel (No. RBR-8-75). Naval Technical Training Command Millington TN Research Branch

  • Kintsch W, Van Dijk TA (1978) Toward a model of text comprehension and production. Psychol Rev 85(5):363

    Article  Google Scholar 

  • Kirman AP, Zimmermann JB (2001) Economics with heterogeneous interacting agents, vol 503. Springer, Heidelberg

    MATH  Google Scholar 

  • Kolmogorov AN (1965) Three approaches to the quantitative definition ofinformation. Probl Inf Transm 1(1):1–7

    MathSciNet  Google Scholar 

  • Koppelman SA (1989) At-risk and passive activity limitations: can complexity be reduced. Tax L Rev 45:97

    Google Scholar 

  • Lall A, Sekar V, Ogihara M, Xu J, Zhang H (2006) Data streaming algorithms for estimating entropy of network traffic. ACM SIGMETRICS Perform Eval Rev 34(1):145–156. ACM

  • Landauer R (1988) A simple measure of complexity. Nature 336:306–307

    Article  Google Scholar 

  • Landauer R (1996) The physical nature of information. Phys Lett A 217(4):188–193

    Article  MATH  MathSciNet  Google Scholar 

  • Lazer D, Pentland AS, Adamic L, Aral S, Barabasi AL, Brewer D, Van Alstyne M (2009) Life in the network: the coming age of computational social science. Science (New York, NY) 323(5915):721

  • Lloyd S, Pagels H (1988) Complexity as thermodynamic depth. Ann Phys 188(1):186–213

    Article  MathSciNet  Google Scholar 

  • Long SB, Swingen JA (1987) An approach to the measurement of tax law complexity. J Am Tax Assoc 8(2):22–36

    Google Scholar 

  • Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Mazzega P, Bourcier D, Bourgine P, Nadah N, Boulet R (2011) A complex-system approach: legal knowledge, ontology, information and networks. In: Sartor G, Casanovas P, Biasiotti M, Fernández-Barrera M (eds) Approaches to legal ontologies. Springer, Netherlands, pp 117–132

  • McCaffery EJ (1990) Holy grail of tax simplification. Wis L Rev 1267–1322

  • McKelvey RD (1976) Intransitivities in multidimensional voting models and some implications for agenda control. J Econ Theory 12(3):472–482

    Article  MATH  MathSciNet  Google Scholar 

  • McKelvey RD (1986) Covering, dominance, and institution-free properties of social choice. Am J Polit Sci 30:283–314

  • Mitchell M (2009) Complexity: a guided tour. Oxford University Press, Oxford

    Google Scholar 

  • Nigam K, Lafferty J, McCallum A (1999) Using maximum entropy for text classification. In: IJCAI-99 workshop on machine learning for information filtering, vol 1, pp 61–67

  • Ohm P (2009) Computer programming and the law: a new research agenda. Vill L Rev 54:117

    MathSciNet  Google Scholar 

  • Ostrom E (1990) Governing the commons: the evolution of institutions for collective action. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Pagallo U (2010) As law goes by: topology, ontology, evolution. In: Casanovas P, Pagallo U, Sartor G, Ajani G (eds) AI approaches to the complexity of legal systems. complex systems, the semantic web, ontologies, argumentation, and dialogue. Springer, Berlin, pp 12–26

  • Page SE (2008) Uncertainty, difficulty, and complexity. J Theor Polit 20(2):115–149

    Article  Google Scholar 

  • Paul DL (1997) Sources of tax complexity: how much simplicity can fundamental tax reform achieve. NCL Rev 76:151

    Google Scholar 

  • Phelan DR (2009) The effect of complexity of law on litigation strategy. In: Masson A, Shariff MJ (eds) Legal strategies. Springer, Berlin, pp 335–351

  • Pitt MM, Slemrod J (1989) The compliance cost of itemizing deductions: evidence from individual tax returns. Am Econ Rev 79:1224–1232

  • Pollock E, Chandler P, Sweller J (2002) Assimilating complex information. Learn Instr 12(1):61–86

    Article  Google Scholar 

  • Poole KT, Rosenthal H (1991) Patterns of congressional voting. Am J Polit Sci 35:228–278

  • Quade D (1979) Using weighted rankings in the analysis of complete blocks with additive block effects. J Am Stat Assoc 74:680

  • Rényi A (1961) On measures of entropy and information. In: Fourth Berkeley symposium on mathematical statistics and probability, pp 547–561

  • Riker WH (1962) The theory of political coalitions, vol 578. Yale University Press, New Haven

    Google Scholar 

  • Rook LW (1993) Laying down the law: canons for drafting complex legislation. Or L Rev 72:663

    Google Scholar 

  • Rothkopf MH, Pekeč A, Harstad RM (1998) Computationally manageable combinational auctions. Manag Sci 44(8):1131–1147

    Article  MATH  Google Scholar 

  • Ruhl JB (2008) Law’s complexity: a primer. Ga St UL Rev 24:885

    Google Scholar 

  • Sanderson M, Croft B (1999) Deriving concept hierarchies from text. In: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 206–213

  • Schenk DH (1989) Simplification for individual taxpayers: problems and proposals. Tax L Rev 45:121

    Google Scholar 

  • Schennach SM (2005) Bayesian exponentially tilted empirical likelihood. Biometrika 92(1):31–46

    Article  MATH  MathSciNet  Google Scholar 

  • Schnotz W, Kürschner C (2008) External and internal representations in the acquisition and use of knowledge: visualization effects on mental model construction. Instr Sci 36(3):175–190

    Article  Google Scholar 

  • Schuck PH (1992) Legal complexity: some causes, consequences, and cures. Duke Law J 42:1–52

  • Schuck PE (2000) The limits of law. Westview Press, Boulder

  • Sen A (1970) Collective choice and social welfare. Holden Day, San Francisco

  • Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423

    Article  MATH  MathSciNet  Google Scholar 

  • Shannon CE (1951) Prediction and entropy of printed English. Bell Syst Tech J 30(1):50–64

    Article  MATH  Google Scholar 

  • Shoham Y, Leyton-Brown K (2009) Multiagent systems: algorithmic, game-theoretic, and logical foundations. Cambridge University Press, Cambridge

  • Si L, Callan J (2001) A statistical model for scientific readability. In: Proceedings of the tenth international conference on Information and knowledge management. ACM, pp 574–576

  • Slemrod J (2005) The etiology of tax complexity: evidence from US state income tax systems. Public Financ Rev 33(3):279–299

    Article  Google Scholar 

  • Slemrod JB, Blumenthal M (1996) The income tax compliance cost of big business. Public Financ Rev 24(4):411–438

    Article  Google Scholar 

  • Soofi ES (2000) Principal information theoretic approaches. J Am Stat Assoc 95(452):1349–1353

    Article  MATH  MathSciNet  Google Scholar 

  • Spiro RJ, Jehng JC (1990) Cognitive flexibility and hypertext: theory and technology for the nonlinear and multidimensional traversal of complex subject matter. Cogn Educ Multimed Explor Ideas High Technol 163–205

  • Stoop R, Stoop N, Bunimovich L (2004) Complexity of dynamics as variability of predictability. J Stat Phys 114(3–4):1127–1137

    Article  MATH  MathSciNet  Google Scholar 

  • Surrey SS (1969) Complexity and the internal revenue code: the problem of the management of tax detail. Law Contemp Probl 34:673–710

  • Sweller J, Chandler P (1994) Why some material is difficult to learn. Cogn Instr 12(3):185–233

    Article  Google Scholar 

  • Tang A, Jackson D, Hobbs J, Chen W, Smith JL, Patel H, Beggs JM (2008) A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro. J Neurosci 28(2):505–518

    Article  Google Scholar 

  • Tress W (2009) Lost laws: what we can’t find in the United States code. Golden Gate UL Rev 40:129

  • Tsallis C (1988) Possible generalization of Boltzmann–Gibbs statistics. J Stat Phys 52(1–2):479–487

    Article  MATH  MathSciNet  Google Scholar 

  • Tukey JW (1957) Sums of random partitions of ranks. Ann Math Stat 23:987–992

  • Tullock G (1995) On the desirable degree of detail in the law. Eur J Law Econ 2(3):199–209

    Article  Google Scholar 

  • Weingast BR, Marshall WJ (1988) The industrial organization of Congress; or, why legislatures, like firms, are not organized as markets. J Polit Econ 96:132–163

  • White MJ (1992) Legal complexity and lawyers’ benefit from litigation. Int Rev Law Econ 12(3):381–395

    Article  Google Scholar 

  • Wright RG (2000) Illusion of simplicity: an explanation of why the law can’t just be less complex. Fla St UL Rev 27:715

    Google Scholar 

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  1. Michigan State University, East Lansing, MI, USA

    Daniel Martin Katz

  2. Lex Predict, LLC, Wayne, NJ, USA

    M. J. Bommarito II

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Katz, D.M., Bommarito, M.J. Measuring the complexity of the law: the United States Code. Artif Intell Law 22, 337–374 (2014). https://doi.org/10.1007/s10506-014-9160-8

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