IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v390y2011i13p2582-2592.html
   My bibliography  Save this article

A network model of knowledge accumulation through diffusion and upgrade

Author

Listed:
  • Zhuang, Enyu
  • Chen, Guanrong
  • Feng, Gang

Abstract

In this paper, we introduce a model to describe knowledge accumulation through knowledge diffusion and knowledge upgrade in a multi-agent network. Here, knowledge diffusion refers to the distribution of existing knowledge in the network, while knowledge upgrade means the discovery of new knowledge. It is found that the population of the network and the number of each agent’s neighbors affect the speed of knowledge accumulation. Four different policies for updating the neighboring agents are thus proposed, and their influence on the speed of knowledge accumulation and the topology evolution of the network are also studied.

Suggested Citation

  • Zhuang, Enyu & Chen, Guanrong & Feng, Gang, 2011. "A network model of knowledge accumulation through diffusion and upgrade," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(13), pages 2582-2592.
    • Handle: RePEc:eee:phsmap:v:390:y:2011:i:13:p:2582-2592
    DOI: 10.1016/j.physa.2011.02.043
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437111001841
    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.2011.02.043?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. Chaomei Chen & Diana Hicks, 2004. "Tracing knowledge diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 59(2), pages 199-211, February.
    2. Cowan, Robin & Jonard, Nicolas, 2004. "Network structure and the diffusion of knowledge," Journal of Economic Dynamics and Control, Elsevier, vol. 28(8), pages 1557-1575, June.
    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. Aistleitner, Matthias & Gräbner, Claudius & Hornykewycz, Anna, 2021. "Theory and empirics of capability accumulation: Implications for macroeconomic modeling," Research Policy, Elsevier, vol. 50(6).
    2. Ioannidis, Evangelos & Varsakelis, Nikos & Antoniou, Ioannis, 2017. "False Beliefs in Unreliable Knowledge Networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 275-295.
    3. Yu Zhang & Morteza Saberi & Elizabeth Chang, 2018. "A semantic-based knowledge fusion model for solution-oriented information network development: a case study in intrusion detection field," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 857-886, November.
    4. Chandra, Praveena & Dong, Andy, 2018. "The relation between knowledge accumulation and technical value in interdisciplinary technologies," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 235-244.
    5. Evangelos Ioannidis & Nikos Varsakelis & Ioannis Antoniou, 2021. "Intelligent Agents in Co-Evolving Knowledge Networks," Mathematics, MDPI, vol. 9(1), pages 1-17, January.
    6. Ioannidis, Evangelos & Varsakelis, Nikos & Antoniou, Ioannis, 2018. "Experts in Knowledge Networks: Central Positioning and Intelligent Selections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 890-905.
    7. Bogner, Kristina, 2019. "Knowledge networks in the German bioeconomy: Network structure of publicly funded R&D networks," Hohenheim Discussion Papers in Business, Economics and Social Sciences 03-2019, University of Hohenheim, Faculty of Business, Economics and Social Sciences.

    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. 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).
    2. Bulent Ozel, 2012. "Collaboration structure and knowledge diffusion in Turkish management academia," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(1), pages 183-206, October.
    3. Yue, Zenghui & Xu, Haiyun & Yuan, Guoting & Pang, Hongshen, 2019. "Modeling study of knowledge diffusion in scientific collaboration networks based on differential dynamics: A case study in graphene field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 375-391.
    4. Stephen Carley & Alan L. Porter, 2012. "A forward diversity index," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 407-427, February.
    5. Stephen Chen, 2009. "Corporate Responsibilities in Internet-Enabled Social Networks," Journal of Business Ethics, Springer, vol. 90(4), pages 523-536, December.
    6. Anil K. Gupta & Paul E. Tesluk & M. Susan Taylor, 2007. "Innovation At and Across Multiple Levels of Analysis," Organization Science, INFORMS, vol. 18(6), pages 885-897, December.
    7. Li, Ziran & Sun, Jiajing & Wang, Shouyang, 2013. "Amplitude-Duration-Persistence Trade-off Relationship for Long Term Bear Stock Markets," MPRA Paper 54177, University Library of Munich, Germany.
    8. Daniele Cassese & Paolo Pin, 2018. "Decentralized Pure Exchange Processes on Networks," Papers 1803.08836, arXiv.org, revised Mar 2022.
    9. Liming Zhao & Haihong Zhang & Wenqing Wu, 2019. "Cooperative knowledge creation in an uncertain network environment based on a dynamic knowledge supernetwork," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 657-685, May.
    10. Rosina Moreno & Ernest Miguélez, 2012. "A Relational Approach To The Geography Of Innovation: A Typology Of Regions," Journal of Economic Surveys, Wiley Blackwell, vol. 26(3), pages 492-516, July.
    11. Mueller, Matthias & Bogner, Kristina & Buchmann, Tobias & Kudic, Muhamed, 2015. "Simulating knowledge diffusion in four structurally distinct networks: An agent-based simulation model," Hohenheim Discussion Papers in Business, Economics and Social Sciences 05-2015, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    12. Mario V. Tomasello & Mauro Napoletano & Antonios Garas & Frank Schweitzer, 2017. "The rise and fall of R&D networks," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 26(4), pages 617-646.
    13. Cantner, Uwe & Graf, Holger, 2006. "The network of innovators in Jena: An application of social network analysis," Research Policy, Elsevier, vol. 35(4), pages 463-480, May.
    14. Dimitris Tsintsaris & Milan Tsompanoglou & Evangelos Ioannidis, 2024. "Dynamics of Social Influence and Knowledge in Networks: Sociophysics Models and Applications in Social Trading, Behavioral Finance and Business," Mathematics, MDPI, vol. 12(8), pages 1-27, April.
    15. Roth, Camille, 2007. "Empiricism for descriptive social network models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(1), pages 53-58.
    16. Stefano Usai & Emanuela Marrocu & Raffaele Paci, 2017. "Networks, Proximities, and Interfirm Knowledge Exchanges," International Regional Science Review, , vol. 40(4), pages 377-404, July.
    17. Nicola Lacetera, 2003. "Incentives and spillovers in R&D activities: an agency-theoretic analysis of industry-university relations," Microeconomics 0312004, University Library of Munich, Germany.
    18. Choe, Hochull & Lee, Duk Hee & Kim, Hee Dae & Seo, Il Won, 2016. "Structural properties and inter-organizational knowledge flows of patent citation network: The case of organic solar cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 361-370.
    19. John McLevey & Alexander V. Graham & Reid McIlroy-Young & Pierson Browne & Kathryn S. Plaisance, 2018. "Interdisciplinarity and insularity in the diffusion of knowledge: an analysis of disciplinary boundaries between philosophy of science and the sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 331-349, October.
    20. Wang, Jean J. & Ye, Fred Y., 2021. "Probing into the interactions between papers and patents of new CRISPR/CAS9 technology: A citation comparison," Journal of Informetrics, Elsevier, vol. 15(4).

    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:390:y:2011:i:13:p:2582-2592. 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.