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      The social physics collective

      editorial
      Author(s): 1 , 2 , 3 ,
      Publication date (Electronic):
      Journal: Scientific Reports
      Publisher: Nature Publishing Group UK
      Keywords: Complex networks, Statistical physics, Social evolution, Climate-change mitigation

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          Abstract

          More than two centuries ago Henri de Saint-Simon envisaged physical laws to describe human societies. Driven by advances in statistical physics, network science, data analysis, and information technology, this vision is becoming a reality. Many of the grandest challenges of our time are of a societal nature, and methods of physics are increasingly playing a central role in improving our understanding of these challenges, and helping us to find innovative solutions. The Social physics Collection at Scientific Reports is dedicated to this research.

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          Statistical physics of human cooperation

          Matjaž Perc, Jillian J. Jordan, David Rand … (2017)
          Article 0 comments Cited 231 times – based on 0 reviews     Review now
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            The network takeover

            Albert-László Barabási (2012)
            Article 0 comments Cited 123 times – based on 0 reviews     Review now
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              Statistical physics of crime: A review

              Maria D'Orsogna, Matjaž Perc (2015)
              Containing the spread of crime in urban societies remains a major challenge. Empirical evidence suggests that, if left unchecked, crimes may be recurrent and proliferate. On the other hand, eradicating a culture of crime may be difficult, especially under extreme social circumstances that impair the creation of a shared sense of social responsibility. Although our understanding of the mechanisms that drive the emergence and diffusion of crime is still incomplete, recent research highlights applied mathematics and methods of statistical physics as valuable theoretical resources that may help us better understand criminal activity. We review different approaches aimed at modeling and improving our understanding of crime, focusing on the nucleation of crime hotspots using partial differential equations, self-exciting point process and agent-based modeling, adversarial evolutionary games, and the network science behind the formation of gangs and large-scale organized crime. We emphasize that statistical physics of crime can relevantly inform the design of successful crime prevention strategies, as well as improve the accuracy of expectations about how different policing interventions should impact malicious human activity that deviates from social norms. We also outline possible directions for future research, related to the effects of social and coevolving networks and to the hierarchical growth of criminal structures due to self-organization.
              Article 0 comments Cited 50 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Matjaž Perc:
                ORCID: http://orcid.org/0000-0002-3087-541X
                matjaz.perc@gmail.com
                Journal
                Journal ID (nlm-ta): Sci Rep
                Journal ID (iso-abbrev): Sci Rep
                Title: Scientific Reports
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2045-2322
                Publication date (Electronic): 12 November 2019
                Publication date PMC-release: 12 November 2019
                Publication date Collection: 2019
                Volume: 9
                Electronic Location Identifier: 16549
                Affiliations
                [1 ]ISNI 0000 0004 0637 0731, GRID grid.8647.d, Faculty of Natural Sciences and Mathematics, , University of Maribor, ; Koroška cesta 160, 2000 Maribor, Slovenia
                [2 ]Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
                [3 ]GRID grid.484678.1, Complexity Science Hub Vienna, ; Josefstädterstraße 39, 1080 Vienna, Austria
                Author information
                http://orcid.org/0000-0002-3087-541X
                Article
                Publisher ID: 53300
                DOI: 10.1038/s41598-019-53300-4
                PMC ID: 6851131
                PubMed ID: 31719644
                SO-VID: 2e3accf3-85a6-47f4-a4e8-df22aefb6c20
                Copyright © © The Author(s) 2019
                License:

                Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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                Subject: Editorial
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                issue-copyright-statement © The Author(s) 2019

                ScienceOpen disciplines: Uncategorized
                Keywords: complex networks,statistical physics,social evolution,climate-change mitigation
                Data availability:
                ScienceOpen disciplines: Uncategorized
                Keywords: complex networks, statistical physics, social evolution, climate-change mitigation

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