Coping with Uncertainties in Technological Learning

References

• Arthur W. B., Ermoliev Y., Kaniovski Y. Path dependent processes and the emergence of macro-structure. Eur. J. Oper. Res. (1987) 30(3):294–303Crossref, Google Scholar
• Arthur W. B. Competing technologies, increasing returns, and lock-in by historical events. Econom. J. (1989) 99(394):116–131Crossref, Google Scholar
• Batten D. F. Complex landscapes of spatial interaction. Ann. Regional Sci. (2001) 35(1):81–111Crossref, Google Scholar
• Gal T., Stewart T. J., Hanne T.Multicriteria Decision Making: Advances in MCDM Models, Algorithms, Theory, and Applications (1999) (Kluwer Academic, Dordrecht, The Netherlands) Crossref, Google Scholar
• Geroski P. A. Models of technology diffusion. Res. Policy (2000) 29(4–5):603–625Crossref, Google Scholar
• Gritsevskyi A., Nakicenovic N. Modeling uncertainty of induced technological change. Energy Policy (2000) 28(13):907–921Crossref, Google Scholar
• Grübler A., Gritsevskyi A. A model of endogenous technological change through uncertain returns on learning (R&D and investments). (1998) . Working paper, International Institute for Applied Systems Analysis, Laxenburg, Austria. http://www.iiasa.ac.at/Research/TNT/WEB/endog.pdfGoogle Scholar
• Grübler A., Gritsevskyi A., Grübler A., Nakicenovic N., Nordhaus W. D. A model of endogenous technological change through uncertain returns on innovation. Technological Change and the Environment (2002) (Resources for the Future, Washington, DC) 280–319Google Scholar
• Haimes Y., Hall W. Multiobjectives in water resource systems analysis: The surrogate worth trade off method. Water Resources Res. (1974) 10(4):615–624Crossref, Google Scholar
• Kypreos S., Barreto L., Capros P., Messner S. ERIS: A model prototype with endogenous technological change. Internat. J. Global Energy (2000) 14(1):374–397Google Scholar
• Loch C. H., Huberman B. A. A punctuated-equilibrium model of technology diffusion. Management Sci. (1999) 45(2):160–177Link, Google Scholar
• Ma T. An agent-based model of endogenous technological change—An extension to the Grübler-Gritsevskyi model. (2006) . Report IR-06-044, International Institute for Applied Systems Analysis, Laxenburg, AustriaGoogle Scholar
• Ma T., Grübler A., Nakamori Y. Modeling technology adoptions for sustainable development under increasing returns, uncertainty, and heterogeneous agents. Eur. J. Oper. Res. (2009) 195(1):296–306Crossref, Google Scholar
• Manne A. S., Barreto L. Learn-by-doing and carbon dioxide abatement. Energy Econom. (2005) 26(4):621–633Crossref, Google Scholar
• McDonald A., Schrattenholzer L. Learning rates for energy technologies. Energy Policy (2001) 29(4):255–261Crossref, Google Scholar
• Messner S. Endogenized technological learning in an energy systems model. J. Evolutionary Econom. (1997) 7(3):291–313Crossref, Google Scholar
• Messner S., Strubegger M., Hake J. F., Kleemann M., Kuckshinrichs W., Martinsen D., Walbeck M. The energy model MESSAGE III. Advances in Systems Analysis: Modelling Energy-Related Emissions on a National and Global Scale (1994) (Forschungszentrum Jülich, Jülich, Germany) 29–49Google Scholar
• Messner S., Golodnikov A., Gritsevskyi A. A stochastic version of the dynamic linear programming model MESSAGE III. Energy (1996) 21(9):775–784Crossref, Google Scholar
• Pajares J., Hernandez-Iglesias C., Lopez-Paredes A. Modelling learning and R&D in innovative environments: A cognitive multi-agent approach. J. Artificial Societies Soc. Simulation (2004) 7(2). http://jasss.soc.surrey.ac.uk/7/2/7.htmlGoogle Scholar
• Seebregts A. D. Energy/environmental modelling using the MARKAL family of models. Proc. OR2001 Conference, Energy, and Environment Session (2001) September 3–5Duisburg, GermanyGoogle Scholar
• Wierzbicki A. P., Makowski M., Wessels J.Model-Based Decision Support Methodology with Environmental Applications (2000) (Kluwer Academic, Dordrecht, The Netherlands) Crossref, Google Scholar