IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v486y2017icp218-241.html
   My bibliography  Save this article

S-Lagrangian dynamics of many-body systems and behavior of social groups: Dominance and hierarchy formation

Author

Listed:
  • Sandler, U.

Abstract

In this paper, we extend our generalized Lagrangian dynamics (i.e., S-Lagrangian dynamics, which can be applied equally to physical and non-physical systems as per Sandler (2014)) to many-body systems. Unlike common Lagrangian dynamics, this is not a trivial task. For many-body systems with S-dependent Lagrangians, the Lagrangian and the corresponding Hamiltonian or energy become vector functions, conjugated momenta become second-order tensors, and the system inevitably develops a hierarchical structure, even if all bodies initially have similar status and Lagrangians. As an application of our theory, we consider dominance and hierarchy formation, which is present in almost all communities of living species. As a biological basis for this application, we assume that the primary motivation of a groups activity is to attempt to cope with stress arising as pressure from the environment and from intrinsic unmet needs of individuals. It has been shown that the S-Lagrangian approach to a group’s evolution naturally leads to formation of linear or despotic dominance hierarchies, depending on differences between individuals in coping with stress. That is, individuals that cope more readily with stress take leadership roles during the evolution. Experimental results in animal groups which support our assumption and findings are considered.

Suggested Citation

  • Sandler, U., 2017. "S-Lagrangian dynamics of many-body systems and behavior of social groups: Dominance and hierarchy formation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 218-241.
  • Handle: RePEc:eee:phsmap:v:486:y:2017:i:c:p:218-241
    DOI: 10.1016/j.physa.2017.05.055
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117305733
    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.2017.05.055?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. A. N. Gorban & E. V. Smirnova & T. A. Tyukina, 2009. "Correlations, Risk and Crisis: From Physiology to Finance," Papers 0905.0129, arXiv.org, revised Aug 2010.
    2. Sandler, U., 2014. "Generalized Lagrangian dynamics of physical and non-physical systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 1-20.
    3. Sandler, U. & Tsitolovsky, L., 2017. "The S-Lagrangian and a theory of homeostasis in living systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 540-553.
    4. Zadeh, Lotfi A., 2006. "Generalized theory of uncertainty (GTU)--principal concepts and ideas," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 15-46, November.
    5. Gorban, Alexander N. & Smirnova, Elena V. & Tyukina, Tatiana A., 2010. "Correlations, risk and crisis: From physiology to finance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3193-3217.
    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. Zhu, Jian & Xi, Jingke & Li, Shihan & Shi, Hongjun & Sun, Yongzheng, 2024. "Time cost estimation for flocking of Cucker–Smale type models with switching protocol," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 636(C).
    2. Sandler, U., 2023. "Evolutionary quantization and matter-antimatter distribution in accelerated expanding of Universe," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).

    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. Xing, Kai & Yang, Xiaoguang, 2020. "Predicting default rates by capturing critical transitions in the macroeconomic system," Finance Research Letters, Elsevier, vol. 32(C).
    2. Giuseppe Orlando & Giovanna Zimatore, 2021. "Recurrence Quantification Analysis of Business Cycles," Dynamic Modeling and Econometrics in Economics and Finance, in: Giuseppe Orlando & Alexander N. Pisarchik & Ruedi Stoop (ed.), Nonlinearities in Economics, chapter 0, pages 269-282, Springer.
    3. Y. Shi & A. N. Gorban & T. Y. Yang, 2013. "Is it possible to predict long-term success with k-NN? Case Study of four market indices (FTSE100, DAX, HANGSENG, NASDAQ)," Papers 1307.8308, arXiv.org.
    4. Heiberger, Raphael H., 2018. "Predicting economic growth with stock networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 102-111.
    5. Sandler, U., 2023. "Evolutionary quantization and matter-antimatter distribution in accelerated expanding of Universe," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    6. Sandler, U. & Tsitolovsky, L., 2017. "The S-Lagrangian and a theory of homeostasis in living systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 540-553.
    7. Sviatoslav R. Rybnikov & Natalya A. Rybnikova & Boris A. Portnov, 2017. "Public Fears in Ukrainian Society," Psychology and Developing Societies, , vol. 29(1), pages 98-123, March.
    8. Ranjeeni, Kumari, 2014. "Sectoral and industrial performance during a stock market crisis," Economic Systems, Elsevier, vol. 38(2), pages 178-193.
    9. Angélique O J Cramer & Claudia D van Borkulo & Erik J Giltay & Han L J van der Maas & Kenneth S Kendler & Marten Scheffer & Denny Borsboom, 2016. "Major Depression as a Complex Dynamic System," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-20, December.
    10. Yao, Hongxing & Memon, Bilal Ahmed, 2019. "Network topology of FTSE 100 Index companies: From the perspective of Brexit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1248-1262.
    11. Giovanna Zimatore & Maria Chiara Gallotta & Matteo Campanella & Piotr H. Skarzynski & Giuseppe Maulucci & Cassandra Serantoni & Marco De Spirito & Davide Curzi & Laura Guidetti & Carlo Baldari & Stavr, 2022. "Detecting Metabolic Thresholds from Nonlinear Analysis of Heart Rate Time Series: A Review," IJERPH, MDPI, vol. 19(19), pages 1-24, October.
    12. Damasco, Achille & Giuliani, Alessandro, 2017. "A resonance based model of biological evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 750-756.
    13. Alexey Y. Bykovsky & Nikolay A. Vasiliev, 2023. "Parametrical T -Gate for Joint Processing of Quantum and Classic Optoelectronic Signals," J, MDPI, vol. 6(3), pages 1-27, July.
    14. Antoaneta Sergueiva, 2013. "Systemic Risk Identification, Modelling, Analysis, and Monitoring: An Integrated Approach," Papers 1310.6486, arXiv.org.
    15. Víctor G. Alfaro García & Anna M. Gil-Lafuente & Gerardo G. Alfaro Calderón, 2015. "A Fuzzy Logic Approach Towards Innovation Measurement," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 9(3), pages 53-71.
    16. Antonio-Pedro Albín-Rodríguez & Adrián-Jesús Ricoy-Cano & Yolanda-María de-la-Fuente-Robles & Macarena Espinilla-Estévez, 2020. "Fuzzy Protoform for Hyperactive Behaviour Detection Based on Commercial Devices," IJERPH, MDPI, vol. 17(18), pages 1-24, September.
    17. María Dolores Peláez-Aguilera & Macarena Espinilla & María Rosa Fernández Olmo & Javier Medina, 2019. "Fuzzy Linguistic Protoforms to Summarize Heart Rate Streams of Patients with Ischemic Heart Disease," Complexity, Hindawi, vol. 2019, pages 1-11, January.
    18. Colubi, Ana & Gonzalez-Rodriguez, Gil, 2007. "Triangular fuzzification of random variables and power of distribution tests: Empirical discussion," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4742-4750, May.
    19. R. A. Aliev & O. H. Huseynov & R. Serdaroglu, 2016. "Ranking of Z-Numbers and Its Application in Decision Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1503-1519, November.
    20. Coppi, Renato & Gil, Maria A. & Kiers, Henk A.L., 2006. "The fuzzy approach to statistical analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 1-14, November.

    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:486:y:2017:i:c:p:218-241. 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.