IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/r5zk7.html
   My bibliography  Save this paper

Strengths and weaknesses of S-curves

Author

Listed:
  • MODIS, THEODORE

Abstract

For the last 22 years I have been fitting logistic S-curves to data points of historical time series at an average rate of about 2–3 per day. This amounts to something between 15,000 and 20,000 fits. Combined with the 40,000 fits of the Monte Carlo study we did with Alain Debecker to quantify the uncertainties in logistic fits [1], probably qualifies me for an entry in the Guinness Book of Records as the man who carried out the greatest number of logistic fits. It hasn't all been fun and games. There have also been blood and tears and not only from human errors. There have been what I came to recognize as “misbehaviors” of reality. I have seen cases where an excellent fit and ensuing forecast were invalidated by later data. But well-established logistic growth reflects the action of a natural law. A disproved forecast is tantamount to violating this law. A law that becomes violated is not much of a law. What is going on? There is something here that needs to be sorted out.

Suggested Citation

  • Modis, Theodore, 2007. "Strengths and weaknesses of S-curves," OSF Preprints r5zk7, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:r5zk7
    DOI: 10.31219/osf.io/r5zk7
    as

    Download full text from publisher

    File URL: https://osf.io/download/61c0987f40147e04389a2821/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/r5zk7?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lin, Hsing-Er & Hsu, I-Chieh & Hsu, Audrey Wenhsin & Chung, Hsi-Mei, 2020. "Creating competitive advantages: Interactions between ambidextrous diversification strategy and contextual factors from a dynamic capability perspective," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    2. Hurmekoski, Elias & Jonsson, Ragnar & Nord, Tomas, 2015. "Context, drivers, and future potential for wood-frame multi-story construction in Europe," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 181-196.
    3. Xu, Jinghong & Zhang, Lin & Ma, Baojun & Wu, Ye, 2016. "Impacts of suppressing guide on information spreading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 922-927.
    4. Palmer, J. & Sorda, G. & Madlener, R., 2015. "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 106-131.
    5. Evangelos Panos & Stavroula Margelou, 2019. "Long-Term Solar Photovoltaics Penetration in Single- and Two-Family Houses in Switzerland," Energies, MDPI, vol. 12(13), pages 1-33, June.
    6. Ewa Lechman, 2013. "Does Technology Adoption Matter For Economic Development? An Empirical Evidence For Latin American Countries," GUT FME Working Paper Series A 17, Faculty of Management and Economics, Gdansk University of Technology.
    7. Ewa Lechman, 2013. "New Technologies Adoption And Diffusion Patterns In Developing Countries. An Empirical Study For The Period 2000-2011," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 8(4), pages 79-106, December.
    8. Ewa Lechman, 2013. "ICTs diffusion trajectories and economic development – an empirical evidence for 46 developing countries," GUT FME Working Paper Series A 18, Faculty of Management and Economics, Gdansk University of Technology.
    9. Rządkowski Grzegorz & Sobczak Lidia, 2020. "A Generalized Logistic Function and Its Applications," Foundations of Management, Sciendo, vol. 12(1), pages 85-92, January.
    10. Xinyuan Zhang & Qing Xie & Chaemin Song & Min Song, 2022. "Mining the evolutionary process of knowledge through multiple relationships between keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2023-2053, April.
    11. Zhang, Rui & Wei, Taoyuan & Sun, Jie & Shi, Qinghua, 2016. "Wave transition in household energy use," Technological Forecasting and Social Change, Elsevier, vol. 102(C), pages 297-308.
    12. Mao, Jin & Liang, Zhentao & Cao, Yujie & Li, Gang, 2020. "Quantifying cross-disciplinary knowledge flow from the perspective of content: Introducing an approach based on knowledge memes," Journal of Informetrics, Elsevier, vol. 14(4).
    13. Dmitry Kucharavy & Eric Schenk & Roland de Guio, 2009. "Long-Run Forecasting of Emerging Technologies with Logistic Models and Growth of Knowledge," Post-Print halshs-00440438, HAL.

    More about this item

    Statistics

    Access and download statistics

    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:osf:osfxxx:r5zk7. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    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.