FORECASTING INNOVATION DIFFUSION WITH NEAR-OPTIMAL BERTALANFFY-PÜTTER MODELS

  • Manfred Kühleitner Institute of Mathematics, DIBB, BOKU Gregor Mendel Strasse 33, A-1180 Vienna, Austria
  • Norbert Brunner University of Natural Resources and Life Sciences (BOKU), Department of Integrative Biology and Biodiversity Research (DIBB), A-1180 Vienna, Austria
  • Katharina Renner-Martin University of Natural Resources and Life Sciences (BOKU), Department of Integrative Biology and Biodiversity Research (DIBB), A-1180 Vienna, Austria
Keywords: Akaike Weight, Bertalanffy-Pütter Differential Equation, Least Squares, Near-Optimal Models, Simulated Annealing, Forecasting, Model-Uncertainty

Abstract

Using a classical example for technology diffusion, the mechanization of agriculture in Spain since 1951, we considered the forecasting-intervals from the near-optimal Bertalanffy-Pütter (BP) models. We used BP-models, as they considerably reduced the hitherto best fit (sum of squared errors) reported in literature. And we considered near-optimal models (their sum of squared errors is almost best), as they allowed to quantify model-uncertainty. This approach supplemented traditional sensitivity analyses (variation of model parameters), as for the present models and data even slight changes in the best-fit parameters resulted in very poorly fitting model curves.

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Published
2020-08-07
How to Cite
Kühleitner, M., Brunner, N., & Renner-Martin, K. (2020). FORECASTING INNOVATION DIFFUSION WITH NEAR-OPTIMAL BERTALANFFY-PÜTTER MODELS. International Journal of Engineering Technologies and Management Research, 7(8), 1-11. https://doi.org/10.29121/ijetmr.v7.i8.2020.745