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Technology adoption with limited foresight and uncertain technological learning

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  • Chen, Huayi
  • Ma, Tieju

Abstract

Most previous optimization models on technology adoption assume perfect foresight over the long term. In reality, decision-makers do not have perfect foresight, and the endogenous driving force of technology adoption is uncertain. With a stylized optimization model, this paper explores the adoption of a new technology, its associated cost dynamics, and technological bifurcations with limited foresight and uncertain technological learning. The study shows that when modeling with limited foresight and technological learning, (1) the longer the length of the decision period, the earlier the adoption of a new technology, and the value of a foresight can be amplified with a high learning rate. However, when the decision period is beyond a certain length, further extending its length has little influence on adopting the new technology; (2) with limited foresight, decisions aiming at minimizing the total cost of each decision period will commonly result in a non-optimal solution from the perspective of the entire decision horizon; and (3) the range of technological bifurcation is much larger than that with perfect foresight, but uncertainty in technological learning tends to reduce the range by removing the early adoption paths of a new technology.

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  • Chen, Huayi & Ma, Tieju, 2014. "Technology adoption with limited foresight and uncertain technological learning," European Journal of Operational Research, Elsevier, vol. 239(1), pages 266-275.
    • Handle: RePEc:eee:ejores:v:239:y:2014:i:1:p:266-275
    DOI: 10.1016/j.ejor.2014.03.031
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    References listed on IDEAS

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    Cited by:

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    3. Jessica Thomsen & Noha Saad Hussein & Arnold Dolderer & Christoph Kost, 2021. "Effect of the Foresight Horizon on Computation Time and Results Using a Regional Energy Systems Optimization Model," Energies, MDPI, vol. 14(2), pages 1-22, January.
    4. Chenhao Fang & Tieju Ma, 2021. "Technology adoption with carbon emission trading mechanism: modeling with heterogeneous agents and uncertain carbon price," Annals of Operations Research, Springer, vol. 300(2), pages 577-600, May.
    5. Chen, Huayi & Ma, Tieju, 2017. "Optimizing systematic technology adoption with heterogeneous agents," European Journal of Operational Research, Elsevier, vol. 257(1), pages 287-296.
    6. Ma, Tieju & Chen, Huayi, 2015. "Adoption of an emerging infrastructure with uncertain technological learning and spatial reconfiguration," European Journal of Operational Research, Elsevier, vol. 243(3), pages 995-1003.
    7. Chen, Huayi & Zhou, P., 2019. "Modeling systematic technology adoption: Can one calibrated representative agent represent heterogeneous agents?," Omega, Elsevier, vol. 89(C), pages 257-270.
    8. Lambert, Jerry & Hanel, Andreas & Fendt, Sebastian & Spliethoff, Hartmut, 2023. "Evaluation of sector-coupled energy systems using different foresight horizons," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).

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