Inviting an author to review:
Find an author and click ‘Invite to review selected article’ near their name.
Search for authorsSearch for similar articles
8
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Complex societies and the growth of the law

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          While many informal factors influence how people interact, modern societies rely upon law as a primary mechanism to formally control human behaviour. How legal rules impact societal development depends on the interplay between two types of actors: the people who create the rules and the people to which the rules potentially apply. We hypothesise that an increasingly diverse and interconnected society might create increasingly diverse and interconnected rules, and assert that legal networks provide a useful lens through which to observe the interaction between law and society. To evaluate these propositions, we present a novel and generalizable model of statutory materials as multidimensional, time-evolving document networks. Applying this model to the federal legislation of the United States and Germany, we find impressive expansion in the size and complexity of laws over the past two and a half decades. We investigate the sources of this development using methods from network science and natural language processing. To allow for cross-country comparisons over time, based on the explicit cross-references between legal rules, we algorithmically reorganise the legislative materials of the United States and Germany into cluster families that reflect legal topics. This reorganisation reveals that the main driver behind the growth of the law in both jurisdictions is the expansion of the welfare state, backed by an expansion of the tax state. Hence, our findings highlight the power of document network analysis for understanding the evolution of law and its relationship with society.

          Related collections

          Most cited references38

          • Record: found
          • Abstract: found
          • Article: not found

          Maps of random walks on complex networks reveal community structure

          To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network-including physics, chemistry, molecular biology, and medicine-information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Statistical physics of social dynamics

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Consensus clustering in complex networks

              The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on the specific random seeds, initial conditions and tie-break rules adopted for their execution. Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods. Here we show that consensus clustering can be combined with any existing method in a self-consistent way, enhancing considerably both the stability and the accuracy of the resulting partitions. This framework is also particularly suitable to monitor the evolution of community structure in temporal networks. An application of consensus clustering to a large citation network of physics papers demonstrates its capability to keep track of the birth, death and diversification of topics.
                Bookmark

                Author and article information

                Contributors
                dkatz3@kentlaw.iit.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                30 October 2020
                30 October 2020
                2020
                : 10
                : 18737
                Affiliations
                [1 ]GRID grid.62813.3e, ISNI 0000 0004 1936 7806, Illinois Tech – Chicago Kent College of Law, ; Chicago, USA
                [2 ]CodeX – The Stanford Center for Legal Informatics, Stanford, USA
                [3 ]GRID grid.419528.3, ISNI 0000 0004 0491 9823, Max Planck Institute for Informatics, ; Saarbrücken, Germany
                [4 ]GRID grid.7700.0, ISNI 0000 0001 2190 4373, Faculty of Law, , Ruprecht-Karls-Universität Heidelberg, ; Heidelberg, Germany
                [5 ]GRID grid.461688.5, ISNI 0000 0000 9215 6192, Bucerius Law School, ; Hamburg, Germany
                Author information
                http://orcid.org/0000-0001-9151-2092
                http://orcid.org/0000-0001-9672-9928
                http://orcid.org/0000-0002-1916-4879
                Article
                73623
                10.1038/s41598-020-73623-x
                7599339
                33127960
                75329f39-e253-492d-b26a-a618772caaca
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 June 2020
                : 16 September 2020
                Funding
                Funded by: Bucerius Law School Interdisciplinary Legal Research Programé
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                complex networks,computational science
                Uncategorized
                complex networks, computational science

                Comments

                Comment on this article