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Modeling Technological Change Under Increasing Returns and Uncertainty

In: Managing Safety of Heterogeneous Systems

Author

Listed:
  • Andrei Gritsevskyi

    (International Atomic Energy Agency)

  • Yuri Ermoliev

    (International Institute for Applied Systems Analysis)

Abstract

The aim of this paper is to analyze methodological challenges involved in modeling of endogenous technological changes with increasing returns and uncertainties by using stylized versions of models. Realistic versions of these models are analytically intractable making it difficult to comprehend the interplay of different assumptions on their outcomes. We demonstrate path-dependences of myopic evolutionary approaches, the infeasibility of straightforward “trial-and-error” processes, and the need for adequate long-term policy assistance. We also show why increasing returns and uncertainties radically offset the rationale for postponed investments in new technologies and how stochastic models cope with systemic risks implicitly induced by interdependencies among uncertainties, technologies, the structure of models, and decisions. The paper demonstrates possible misleading character of alternative models of uncertainties. It shows the need for proper modeling of long-term random horizons, corresponding discounting, security constraints and requirements of robustness by using systemic valuations and “distribution free” stochastic programming/optimization.

Suggested Citation

  • Andrei Gritsevskyi & Yuri Ermoliev, 2012. "Modeling Technological Change Under Increasing Returns and Uncertainty," Lecture Notes in Economics and Mathematical Systems, in: Yuri Ermoliev & Marek Makowski & Kurt Marti (ed.), Managing Safety of Heterogeneous Systems, edition 127, pages 109-136, Springer.
    • Handle: RePEc:spr:lnechp:978-3-642-22884-1_6
    DOI: 10.1007/978-3-642-22884-1_6
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    Citations

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    Cited by:

    1. Emilio Cano & Javier Moguerza & Tatiana Ermolieva & Yuri Ermoliev, 2014. "Energy efficiency and risk management in public buildings: strategic model for robust planning," Computational Management Science, Springer, vol. 11(1), pages 25-44, January.
    2. Emilio L. Cano & Javier M. Moguerza & Tatiana Ermolieva & Yurii Yermoliev, 2017. "A strategic decision support system framework for energy-efficient technology investments," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 249-270, July.
    3. Tatiana Ermolieva & Petr Havlik & Yuri Ermoliev & Nikolay Khabarov & Michael Obersteiner, 2021. "Robust Management of Systemic Risks and Food-Water-Energy-Environmental Security: Two-Stage Strategic-Adaptive GLOBIOM Model," Sustainability, MDPI, vol. 13(2), pages 1-16, January.

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