IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0064703.html
   My bibliography  Save this article

The Phenomenology of Specialization of Criminal Suspects

Author

Listed:
  • Michele Tumminello
  • Christofer Edling
  • Fredrik Liljeros
  • Rosario N Mantegna
  • Jerzy Sarnecki

Abstract

A criminal career can be either general, with the criminal committing different types of crimes, or specialized, with the criminal committing a specific type of crime. A central problem in the study of crime specialization is to determine, from the perspective of the criminal, which crimes should be considered similar and which crimes should be considered distinct. We study a large set of Swedish suspects to empirically investigate generalist and specialist behavior in crime. We show that there is a large group of suspects who can be described as generalists. At the same time, we observe a non-trivial pattern of specialization across age and gender of suspects. Women are less prone to commit crimes of certain types, and, for instance, are more prone to specialize in crimes related to fraud. We also find evidence of temporal specialization of suspects. Older persons are more specialized than younger ones, and some crime types are preferentially committed by suspects of different ages.

Suggested Citation

  • Michele Tumminello & Christofer Edling & Fredrik Liljeros & Rosario N Mantegna & Jerzy Sarnecki, 2013. "The Phenomenology of Specialization of Criminal Suspects," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-8, May.
    • Handle: RePEc:plo:pone00:0064703
    DOI: 10.1371/journal.pone.0064703
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0064703
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0064703&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0064703?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Vasiliki Plerou & Parameswaran Gopikrishnan & Bernd Rosenow & Luis A. Nunes Amaral & H. Eugene Stanley, 1999. "Universal and non-universal properties of cross-correlations in financial time series," Papers cond-mat/9902283, arXiv.org.
    2. Michele Tumminello & Salvatore Miccichè & Fabrizio Lillo & Jyrki Piilo & Rosario N Mantegna, 2011. "Statistically Validated Networks in Bipartite Complex Systems," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tanskanen, Maiju & Aaltonen, Mikko, 2022. "Social correlates of specialized versus versatile offending patterns in intimate partner violence: A register-based study in Finland," Journal of Criminal Justice, Elsevier, vol. 81(C).
    2. Michele Tumminello & Andrea Consiglio & Pietro Vassallo & Riccardo Cesari & Fabio Farabullini, 2023. "Insurance fraud detection: A statistically validated network approach," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 90(2), pages 381-419, June.
    3. Coronnello, Claudia & Tumminello, Michele & Miccichè, Salvatore, 2016. "Gene-based and semantic structure of the Gene Ontology as a complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 313-328.
    4. Puccio, Elena & Pajala, Antti & Piilo, Jyrki & Tumminello, Michele, 2016. "Structure and evolution of a European Parliament via a network and correlation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 167-185.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jovanovic, Franck & Mantegna, Rosario N. & Schinckus, Christophe, 2019. "When financial economics influences physics: The role of Econophysics," International Review of Financial Analysis, Elsevier, vol. 65(C).
    2. Basnarkov, Lasko & Stojkoski, Viktor & Utkovski, Zoran & Kocarev, Ljupco, 2020. "Lead–lag relationships in foreign exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    3. Basnarkov, Lasko & Stojkoski, Viktor & Utkovski, Zoran & Kocarev, Ljupco, 2019. "Correlation patterns in foreign exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1026-1037.
    4. Paul Ormerod, 2010. "La crisis actual y la culpabilidad de la teoría macroeconómica," Revista de Economía Institucional, Universidad Externado de Colombia - Facultad de Economía, vol. 12(22), pages 111-128, January-J.
    5. Han, Rui-Qi & Li, Ming-Xia & Chen, Wei & Zhou, Wei-Xing & Stanley, H. Eugene, 2019. "Structural properties of statistically validated empirical information networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 747-756.
    6. Andrea Flori & Fabrizio Lillo & Fabio Pammolli & Alessandro Spelta, 2021. "Better to stay apart: asset commonality, bipartite network centrality, and investment strategies," Annals of Operations Research, Springer, vol. 299(1), pages 177-213, April.
    7. Muchnik, Lev & Bunde, Armin & Havlin, Shlomo, 2009. "Long term memory in extreme returns of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(19), pages 4145-4150.
    8. Michelle B Graczyk & Sílvio M Duarte Queirós, 2017. "Intraday seasonalities and nonstationarity of trading volume in financial markets: Collective features," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-23, July.
    9. Ixandra Achitouv, 2025. "Dynamical analysis of financial stocks network: Improving forecasting using network properties," PLOS ONE, Public Library of Science, vol. 20(5), pages 1-23, May.
    10. Tobias Wand & Oliver Kamps & Hiroshi Iyetomi, 2024. "Causal Hierarchy in the Financial Market Network -- Uncovered by the Helmholtz-Hodge-Kodaira Decomposition," Papers 2408.12839, arXiv.org.
    11. Adele Ravagnani & Fabrizio Lillo & Paola Deriu & Piero Mazzarisi & Francesca Medda & Antonio Russo, 2024. "Dimensionality reduction techniques to support insider trading detection," Papers 2403.00707, arXiv.org, revised May 2024.
    12. Andrés García-Medina & Graciela González Farías, 2020. "Transfer entropy as a variable selection methodology of cryptocurrencies in the framework of a high dimensional predictive model," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-31, January.
    13. Tobias Wand & Martin He{ss}ler & Oliver Kamps, 2022. "Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data," Papers 2208.14106, arXiv.org, revised Mar 2023.
    14. Antti J. Tanskanen & Jani Lukkarinen & Kari Vatanen, 2016. "Random selection of factors preserves the correlation structure in a linear factor model to a high degree," Papers 1604.05896, arXiv.org, revised Dec 2018.
    15. Sgrignoli, Paolo & Metulini, Rodolfo & Schiavo, Stefano & Riccaboni, Massimo, 2015. "The relation between global migration and trade networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 245-260.
    16. Pafka, Szilárd & Kondor, Imre, 2001. "Evaluating the RiskMetrics methodology in measuring volatility and Value-at-Risk in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 305-310.
    17. Corrêa, Edilson A. & Marinho, Vanessa Q. & Amancio, Diego R., 2020. "Semantic flow in language networks discriminates texts by genre and publication date," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    18. Maria Letizia Bertotti & Amit K Chattopadhyay & Giovanni Modanese, 2017. "Economic inequality and mobility for stochastic models with multiplicative noise," Papers 1702.08391, arXiv.org.
    19. Andreas Muhlbacher & Thomas Guhr, 2018. "Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations," Papers 1803.00261, arXiv.org.
    20. Neeraj, & Panigrahi, Prasanta K., 2017. "Causality and correlations between BSE and NYSE indexes: A Janus faced relationship," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 284-313.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0064703. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.