7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A master equation for power laws

      research-article
      1 , 2 , , 3
      Royal Society Open Science
      The Royal Society
      power laws, Markov chains, urn model

      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

          We propose a new mechanism for generating power laws. Starting from a random walk, we first outline a simple derivation of the Fokker–Planck equation. By analogy, starting from a certain Markov chain, we derive a master equation for power laws that describes how the number of cascades changes over time (cascades are consecutive transitions that end when the initial state is reached). The partial differential equation has a closed form solution which gives an explicit dependence of the number of cascades on their size and on time. Furthermore, the power law solution has a natural cut-off, a feature often seen in empirical data. This is due to the finite size a cascade can have in a finite time horizon. The derivation of the equation provides a justification for an exponent equal to 2, which agrees well with several empirical distributions, including Richardson’s Law on the size and frequency of deadly conflicts. Nevertheless, the equation can be solved for any exponent value. In addition, we propose an urn model where the number of consecutive ball extractions follows a power law. In all cases, the power law is manifest over the entire range of cascade sizes, as shown through log–log plots in the frequency and rank distributions.

          Related collections

          Most cited references80

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

          Emergence of Scaling in Random Networks

          Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Statistical Inference Using Extreme Order Statistics

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

              Power laws, Pareto distributions and Zipf's law

              MEJ Newman (2005)
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Journal
                R Soc Open Sci
                R Soc Open Sci
                RSOS
                royopensci
                Royal Society Open Science
                The Royal Society
                2054-5703
                December 7, 2022
                December 2022
                December 7, 2022
                : 9
                : 12
                : 220531
                Affiliations
                [ 1 ] Centre for the Study of Existential Risk, University of Cambridge, , Cambridge, UK
                [ 2 ] Odyssean Institute, , London, UK
                [ 3 ] Carlo Cattaneo University LIUC, , Castellanza, Lombardia, Italy
                Author information
                http://orcid.org/0000-0002-7513-3479
                Article
                rsos220531
                10.1098/rsos.220531
                9727680
                36483760
                2c2ddef8-5b30-4cfc-b7a4-79114227b800
                © 2022 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : April 22, 2022
                : November 14, 2022
                Funding
                Funded by: Grantham Foundation for the Protection of the Environment, http://dx.doi.org/10.13039/100008118;
                Categories
                1004
                1008
                1009
                120
                6
                174
                Mathematics
                Research Articles

                power laws,markov chains,urn model
                power laws, markov chains, urn model

                Comments

                Comment on this article