IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v567y2021ics0378437120309766.html
   My bibliography  Save this article

Scale-free networks, 1/f dynamics, and nonlinear conflict size scaling from an agent-based simulation model of societal-scale bilateral conflict and cooperation

Author

Listed:
  • Fleming, Sean W.

Abstract

An agent-based model is presented that mechanistically simulates social interactions across two partially coupled lattices, each containing a mixture of individualists, networkers, and reciprocators. Numerical experiments reveal evidence for two spontaneously emergent and widely relevant complex behaviors: self-organized criticality generating fractal (1/f) dynamics, and a scale-free (power-law degree distribution) network, adding to the short list of generative mechanisms for these phenomena. The model may also suggest explanatory hypotheses for two sociological puzzles: Richardson’s scaling law for war size; and an inverse relationship between actor scale and water resource conflict, potentially relevant to this century’s prognosticated water wars. Adjusting a handful of model parameters yields a diverse set of fundamentally different behaviors, perhaps implying model applicability to a wide range of social systems and that comparatively simple social engineering steps could conceivably induce large social shifts.

Suggested Citation

  • Fleming, Sean W., 2021. "Scale-free networks, 1/f dynamics, and nonlinear conflict size scaling from an agent-based simulation model of societal-scale bilateral conflict and cooperation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    • Handle: RePEc:eee:phsmap:v:567:y:2021:i:c:s0378437120309766
    DOI: 10.1016/j.physa.2020.125678
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120309766
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2020.125678?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
    2. Anna D. Broido & Aaron Clauset, 2019. "Scale-free networks are rare," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    3. Petter Holme, 2019. "Rare and everywhere: Perspectives on scale-free networks," Nature Communications, Nature, vol. 10(1), pages 1-3, December.
    4. Cederman, Lars-Erik, 2003. "Modeling the Size of Wars: From Billiard Balls to Sandpiles," American Political Science Review, Cambridge University Press, vol. 97(1), pages 135-150, February.
    Full references (including those not matched with items on IDEAS)

    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. Meng, Xiangyi & Zhou, Bin, 2023. "Scale-free networks beyond power-law degree distribution," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Jiang, Xiong-Fei & Xiong, Long & Bai, Ling & Lin, Jie & Zhang, Jing-Feng & Yan, Kun & Zhu, Jia-Zhen & Zheng, Bo & Zheng, Jian-Jun, 2022. "Structure and dynamics of human complication-disease network," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    3. Valero, Jordi & Pérez-Casany, Marta & Duarte-López, Ariel, 2022. "The Zipf-Polylog distribution: Modeling human interactions through social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    4. Bin Zhou & Petter Holme & Zaiwu Gong & Choujun Zhan & Yao Huang & Xin Lu & Xiangyi Meng, 2023. "The nature and nurture of network evolution," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    5. Koponen, Ismo T. & Palmgren, Elina & Keski-Vakkuri, Esko, 2021. "Characterising heavy-tailed networks using q-generalised entropy and q-adjacency kernels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    6. Johnston, Josh & Andersen, Tim, 2022. "Random processes with high variance produce scale free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    7. John Barkoulas & Christopher Baum & Mustafa Caglayan, 1999. "Fractional monetary dynamics," Applied Economics, Taylor & Francis Journals, vol. 31(11), pages 1393-1400.
    8. Jan Babecký & Fabrizio Coricelli & Roman Horváth, 2009. "Assessing Inflation Persistence: Micro Evidence on an Inflation Targeting Economy," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 59(2), pages 102-127, June.
    9. SILVESTRINI, Andrea & VEREDAS, David, 2005. "Temporal aggregation of univariate linear time series models," LIDAM Discussion Papers CORE 2005059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Michelacci, Claudio & Zaffaroni, Paolo, 2000. "(Fractional) beta convergence," Journal of Monetary Economics, Elsevier, vol. 45(1), pages 129-153, February.
    11. Gil-Alana, L.A., 2006. "Fractional integration in daily stock market indexes," Review of Financial Economics, Elsevier, vol. 15(1), pages 28-48.
    12. Dufrenot, Gilles & Guegan, Dominique & Peguin-Feissolle, Anne, 2005. "Modelling squared returns using a SETAR model with long-memory dynamics," Economics Letters, Elsevier, vol. 86(2), pages 237-243, February.
    13. Claudio Morana & Giacomo Sbrana, 2017. "Temperature Anomalies, Radiative Forcing and ENSO," Working Papers 2017.09, Fondazione Eni Enrico Mattei.
    14. Richard T. Baillie & Fabio Calonaci & Dooyeon Cho & Seunghwa Rho, 2019. "Long Memory, Realized Volatility and HAR Models," Working Papers 881, Queen Mary University of London, School of Economics and Finance.
    15. Eric Hillebrand & Marcelo C. Medeiros, 2016. "Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 23-41, January.
    16. A. M. M. Shahiduzzaman Quoreshi & Reaz Uddin & Naushad Mamode Khan, 2019. "Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data—Under Conditional Heteroskedasticity Framework," JRFM, MDPI, vol. 12(2), pages 1-13, April.
    17. Mensi, Walid & Hammoudeh, Shawkat & Yoon, Seong-Min, 2014. "Structural breaks and long memory in modeling and forecasting volatility of foreign exchange markets of oil exporters: The importance of scheduled and unscheduled news announcements," International Review of Economics & Finance, Elsevier, vol. 30(C), pages 101-119.
    18. Claudio, Morana & Giacomo, Sbrana, 2017. "Some Financial Implications of Global Warming: An Empirical Assessment," Working Papers 377, University of Milano-Bicocca, Department of Economics, revised 25 Dec 2017.
    19. Jean-Philippe Gervais, 2011. "Disentangling nonlinearities in the long- and short-run price relationships: an application to the US hog/pork supply chain," Applied Economics, Taylor & Francis Journals, vol. 43(12), pages 1497-1510.
    20. Maria Kalli & Jim Griffin, 2015. "Flexible Modeling of Dependence in Volatility Processes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 102-113, January.

    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:eee:phsmap:v:567:y:2021:i:c:s0378437120309766. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.