IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v164y2022ics0960077922008876.html
   My bibliography  Save this article

Global stability and optimal control analysis of a knowledge transmission model in multilayer networks

Author

Listed:
  • Mei, Jun
  • Wang, Sixin
  • Xia, Dan
  • Hu, Junhao

Abstract

Knowledge dissemination plays an important role in many aspects. The control strategy to improve the performance of knowledge transmission in couple networks is a meaningful work, which is little consideration in the existing work. This paper addresses the problem of optimal control for a class of knowledge transmission models. Firstly, the multilayer complex networks are built according to how knowledge is acquired. Secondly, inspiration from the spread of a disease, a model of knowledge transmission is established. Moreover, the basic reproduction number R0, knowledge-free equilibrium (KFE), and knowledge endemic equilibrium (KEE), as well as their stability, are deduced. Then, the imposition of optimal control, including improving the digestion and absorption of knowledge contacts and increasing the review rate of knowledge-forgotten persons, can increase the number of knowledge communicators. Afterward, Pontryagin’s maximum principle is used to deal with the nonlinear optimal control problem. Finally, through numerical simulations, the stability of the equilibriums are confirmed, the effect of knowledge dissemination is the best and the range of the knowledge dissemination is widest when two control strategies are applied at the same time.

Suggested Citation

  • Mei, Jun & Wang, Sixin & Xia, Dan & Hu, Junhao, 2022. "Global stability and optimal control analysis of a knowledge transmission model in multilayer networks," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
  • Handle: RePEc:eee:chsofr:v:164:y:2022:i:c:s0960077922008876
    DOI: 10.1016/j.chaos.2022.112708
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077922008876
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2022.112708?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. Huo, Liang’an & Song, Naixiang, 2016. "Dynamical interplay between the dissemination of scientific knowledge and rumor spreading in emergency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 73-84.
    2. Liao, Shi-Gen & Yi, Shu-Ping, 2021. "Modeling and analyzing knowledge transmission process considering free-riding behavior of knowledge acquisition: A waterborne disease approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    3. Farid, May & Noguchi, Lori, 2022. "Knowledge communities and policy influence in China," World Development, Elsevier, vol. 150(C).
    4. Cao, Bin & Han, Shui-hua & Jin, Zhen, 2016. "Modeling of knowledge transmission by considering the level of forgetfulness in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 277-287.
    5. Qiao, Tong & Shan, Wei & Zhang, Mingli & Liu, Chen, 2019. "How to facilitate knowledge diffusion in complex networks: The roles of network structure, knowledge role distribution and selection rule," International Journal of Information Management, Elsevier, vol. 47(C), pages 152-167.
    6. Liao, Shi-Gen & Yi, Shu-Ping, 2021. "Modeling and analysis knowledge transmission process in complex networks by considering internalization mechanism," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    7. Ikujiro Nonaka, 1994. "A Dynamic Theory of Organizational Knowledge Creation," Organization Science, INFORMS, vol. 5(1), pages 14-37, February.
    8. Romer, Paul M, 1990. "Endogenous Technological Change," Journal of Political Economy, University of Chicago Press, vol. 98(5), pages 71-102, October.
    9. Wang, Sheng-Fu & Hu, Lin & Nie, Lin-Fei, 2021. "Global dynamics and optimal control of an age-structure Malaria transmission model with vaccination and relapse," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    10. Wang, Haiying & Wang, Jun & Small, Michael, 2018. "Knowledge transmission model with differing initial transmission and retransmission process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 478-488.
    11. Wang, Haiying & Moore, Jack Murdoch & Wang, Jun & Small, Michael, 2021. "The distinct roles of initial transmission and retransmission in the persistence of knowledge in complex networks," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    12. Wang, Haiying & Wang, Jun & Ding, Liting & Wei, Wei, 2017. "Knowledge transmission model with consideration of self-learning mechanism in complex networks," Applied Mathematics and Computation, Elsevier, vol. 304(C), pages 83-92.
    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. Zhu, Hongmiao & Jin, Zhen, 2023. "A dynamics model of knowledge dissemination in a WeChat Group from perspective of duplex networks," Applied Mathematics and Computation, Elsevier, vol. 454(C).
    2. Zhu, Hongmiao & Jin, Zhen & Yan, Xin, 2023. "A dynamics model of coupling transmission for multiple different knowledge in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
    3. Zhu, He & Ma, Jing, 2018. "Knowledge diffusion in complex networks by considering time-varying information channels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 225-235.
    4. Wang, Sixin & Mei, Jun & Xia, Dan & Yang, Zhanying & Hu, Junhao, 2022. "Finite-time optimal feedback control mechanism for knowledge transmission in complex networks via model predictive control," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    5. Wang, Haiying & Moore, Jack Murdoch & Small, Michael & Wang, Jun & Yang, Huijie & Gu, Changgui, 2022. "Epidemic dynamics on higher-dimensional small world networks," Applied Mathematics and Computation, Elsevier, vol. 421(C).
    6. Liao, Shi-Gen & Yi, Shu-Ping, 2021. "Modeling and analysis knowledge transmission process in complex networks by considering internalization mechanism," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    7. Wang, Haiying & Moore, Jack Murdoch & Wang, Jun & Small, Michael, 2021. "The distinct roles of initial transmission and retransmission in the persistence of knowledge in complex networks," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    8. Feldman, Maryann P. & Kogler, Dieter F., 2010. "Stylized Facts in the Geography of Innovation," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 1, chapter 0, pages 381-410, Elsevier.
    9. Jose M Barrutia & Carmen Echebarria & Ainhize Gilsanz, 2011. "Social capital and innovation: an empirical analysis in the context of European regions," ERSA conference papers ersa10p1347, European Regional Science Association.
    10. Wang, Haiying & Wang, Jun & Small, Michael & Moore, Jack Murdoch, 2019. "Review mechanism promotes knowledge transmission in complex networks," Applied Mathematics and Computation, Elsevier, vol. 340(C), pages 113-125.
    11. Koschatzky, Knut, 2000. "The regionalisation of innovation policy in Germany: theoretical foundations and recent experience," Working Papers "Firms and Region" R1/2000, Fraunhofer Institute for Systems and Innovation Research (ISI).
    12. Ferreira, João J.M. & Fernandes, Cristina I. & Veiga, Pedro Mota, 2024. "The effects of knowledge spillovers, digital capabilities, and innovation on firm performance: A moderated mediation model," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    13. Yan Yan & Jiancheng Guan, 2018. "How multiple networks help in creating knowledge: evidence from alternative energy patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 51-77, April.
    14. Xiaodan Kong & Qi Xu & Tao Zhu, 2019. "Dynamic Evolution of Knowledge Sharing Behavior among Enterprises in the Cluster Innovation Network Based on Evolutionary Game Theory," Sustainability, MDPI, vol. 12(1), pages 1-23, December.
    15. Evita Pangaribowo & Nicolas Gerber & Pascal Tillie, 2013. "Assessing the FNS impacts of technological and institutional innovations and future innovation trends," FOODSECURE Working papers 11, LEI Wageningen UR.
    16. Wang, Haiying & Wang, Jun & Small, Michael, 2018. "Knowledge transmission model with differing initial transmission and retransmission process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 478-488.
    17. Schmidt, Tobias & Sofka, Wolfgang, 2009. "Liability of foreignness as a barrier to knowledge spillovers: Lost in translation?," Journal of International Management, Elsevier, vol. 15(4), pages 460-474, December.
    18. Lattacher, Wolfgang & Gregori, Patrick & Holzmann, Patrick & Schwarz, Erich J., 2021. "Knowledge spillover in entrepreneurial emergence: A learning perspective," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    19. Zhao, Jianyu & Wu, Guangdong & Xi, Xi & Na, Qi & Liu, Weiwei, 2018. "How collaborative innovation system in a knowledge-intensive competitive alliance evolves? An empirical study on China, Korea and Germany," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 128-146.
    20. Aija Leiponen, 2006. "Organization of knowledge exchange: An empirical study of knowledge-intensive business service relationships," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 15(4-5), pages 443-464.

    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:chsofr:v:164:y:2022:i:c:s0960077922008876. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.