IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v488y2017icp112-120.html
   My bibliography  Save this article

The mechanisms of labor division from the perspective of individual optimization

Author

Listed:
  • Zhu, Lirong
  • Chen, Jiawei
  • Di, Zengru
  • Chen, Liujun
  • Liu, Yan
  • Stanley, H. Eugene

Abstract

Although the tools of complexity research have been applied to the phenomenon of labor division, its underlying mechanisms are still unclear. Researchers have used evolutionary models to study labor division in terms of global optimization, but focusing on individual optimization is a more realistic, real-world approach. We do this by first developing a multi-agent model that takes into account information-sharing and learning-by-doing and by using simulations to demonstrate the emergence of labor division. We then use a master equation method and find that the computational results are consistent with the results of the simulation. Finally we find that the core underlying mechanisms that cause labor division are learning-by-doing, information cost, and random fluctuation.

Suggested Citation

  • Zhu, Lirong & Chen, Jiawei & Di, Zengru & Chen, Liujun & Liu, Yan & Stanley, H. Eugene, 2017. "The mechanisms of labor division from the perspective of individual optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 488(C), pages 112-120.
    • Handle: RePEc:eee:phsmap:v:488:y:2017:i:c:p:112-120
    DOI: 10.1016/j.physa.2017.06.024
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117306659
    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.2017.06.024?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. Kang Liu & N. Lubbers & W. Klein & J. Tobochnik & B. Boghosian & Harvey Gould, 2013. "The Effect of Growth On Equality in Models of the Economy," Papers 1305.0794, arXiv.org.
    2. Gao, Ya-Chun & Cai, Shi-Min & Lü, Linyuan & Wang, Bing-Hong, 2013. "Evolutionary model on market ecology of investors and investments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3385-3391.
    3. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    4. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frédéric Abergel, 2011. "Econophysics review: II. Agent-based models," Post-Print hal-00621059, HAL.
    5. Hiroshi Iyetomi & Hideaki Aoyama & Yoshi Fujiwara & Yuichi Ikeda & Wataru Souma, 2009. "Agent-Based Model Approach to Complex Phenomena in Real Economy," Papers 0901.1794, arXiv.org.
    6. Wu, Jinshan & Di, Zengru & Yang, Zhanru, 2003. "Division of labor as the result of phase transition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 323(C), pages 663-676.
    7. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2011. "Econophysics review: II. Agent-based models," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 1013-1041.
    8. Simon, Julian L. & Steinmann, Gunter, 1984. "The economic implications of learning-by-doing for population size and growth," European Economic Review, Elsevier, vol. 26(1-2), pages 167-185.
    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. de Oliveira, Viviane M. & Campos, Paulo R.A., 2019. "The emergence of division of labor in a structured response threshold model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 153-162.
    2. Qin, Shipeng & Zhang, Gang & Tian, Haiyan & Hu, Wenjun & Zhang, Xiaoming, 2020. "Dynamics of asymmetric division of labor game with environmental feedback," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(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. Christoph J. Borner & Ingo Hoffmann & John H. Stiebel, 2024. "A closer look at the chemical potential of an ideal agent system," Papers 2401.09233, arXiv.org.
    2. Kiran Sharma & Subhradeep Das & Anirban Chakraborti, 2017. "Global Income Inequality and Savings: A Data Science Perspective," Papers 1801.00253, arXiv.org, revised Aug 2018.
    3. Marconi, G. & de Grip, A., 2014. "Education and growth with learning by doing," ROA Research Memorandum 010, Maastricht University, Research Centre for Education and the Labour Market (ROA).
    4. Ivan Jericevich & Murray McKechnie & Tim Gebbie, 2021. "Calibrating an adaptive Farmer-Joshi agent-based model for financial markets," Papers 2104.09863, arXiv.org.
    5. Ban Zheng & François Roueff & Frédéric Abergel, 2014. "Ergodicity and scaling limit of a constrained multivariate Hawkes process," Post-Print hal-00777941, HAL.
    6. Ivan Jericevich & Patrick Chang & Tim Gebbie, 2021. "Simulation and estimation of a point-process market-model with a matching engine," Papers 2105.02211, arXiv.org, revised Aug 2021.
    7. Seemann, Lars & Hua, Jia-Chen & McCauley, Joseph L. & Gunaratne, Gemunu H., 2012. "Ensemble vs. time averages in financial time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6024-6032.
    8. A. Lykov & S. Muzychka & K. Vaninsky, 2012. "Investor's sentiment in multi-agent model of the continuous double auction," Papers 1208.3083, arXiv.org, revised Feb 2016.
    9. A. O. Glekin & A. Lykov & K. L. Vaninsky, 2014. "On Simulation of Various Effects in Consolidated Order Book," Papers 1402.4150, arXiv.org.
    10. Hong Guo & Jianwu Lin & Fanlin Huang, 2023. "Market Making with Deep Reinforcement Learning from Limit Order Books," Papers 2305.15821, arXiv.org.
    11. Fei Cao & Sebastien Motsch, 2021. "Derivation of wealth distributions from biased exchange of money," Papers 2105.07341, arXiv.org.
    12. Pierre Gosselin & Aïleen Lotz & Marc Wambst, 2020. "A path integral approach to business cycle models with large number of agents," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(4), pages 899-942, October.
    13. Dias, Thiago & Gonçalves, Sebastián, 2024. "Effectiveness of wealth-based vs exchange-based tax systems in reducing inequality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).
    14. Moura, N.J. & Ribeiro, Marcelo B., 2013. "Testing the Goodwin growth-cycle macroeconomic dynamics in Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2088-2103.
    15. Florin Turcaș & Florin Cornel Dumiter & Marius Boiță, 2022. "Econophysics Techniques and Their Applications on the Stock Market," Mathematics, MDPI, vol. 10(6), pages 1-25, March.
    16. Pierre Gosselin & Aïleen Lotz & Marc Wambst, 2019. "A Statistical Field Approach to Capital Accumulation," Working Papers hal-02280634, HAL.
    17. Stein, Julian Alexander Cornelius & Braun, Dieter, 2019. "Stability of a time-homogeneous system of money and antimoney in an agent-based random economy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 232-249.
    18. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    19. Hua, Jia-Chen & Chen, Lijian & Falcon, Liberty & McCauley, Joseph L. & Gunaratne, Gemunu H., 2015. "Variable diffusion in stock market fluctuations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 221-233.
    20. Zhang, Jiu & Jin, Li-Fu & Zheng, Bo & Li, Yan & Jiang, Xiong-Fei, 2022. "Simplified calculations of time correlation functions in non-stationary complex financial systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).

    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:488:y:2017:i:c:p:112-120. 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.