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Strengths and weaknesses of S-curves

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  • 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
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    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. 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.
    4. repec:gdk:wpaper:17 is not listed on IDEAS
    5. 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.
    6. repec:gdk:wpaper:5 is not listed on IDEAS
    7. 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.
    8. 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.
    9. 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.
    10. repec:gdk:wpaper:18 is not listed on IDEAS
    11. Rządkowski Grzegorz & Sobczak Lidia, 2020. "A Generalized Logistic Function and its Applications," Foundations of Management, Sciendo, vol. 12(1), pages 85-92, January.
    12. 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.
    13. 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).
    14. 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.

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