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Fuzzy Logic Decision-Making Model for Technology Foresight

Author

Listed:
  • Vladyslav Sotnyk
  • Artem Kupchyn
  • Viktor Trynchuk
  • Vladimer Glonti
  • Larisa Belinskaja

Abstract

A new decision-making model in technology foresight based on fuzzy logic is proposed. The choice of technology as disruptive or critical doesn’t depend on the subjective expert’s opinion, but bases on the mathematically justified limits of technologies criticality. The basis of the model is the fuzzy inference system by Mamdani algorithm. Five of the most important criteria of criticality have been used as input linguistic variables. A new approach to defining membership functions based on equidistant derivative points is proposed and described in detail. The functioning of the model is considered by example. The influence of the application of different membership functions on the criticality assessment is shown. A comparison between the fuzzy model and classic expert model is also conducted.

Suggested Citation

  • Vladyslav Sotnyk & Artem Kupchyn & Viktor Trynchuk & Vladimer Glonti & Larisa Belinskaja, 2022. "Fuzzy Logic Decision-Making Model for Technology Foresight," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 139-159.
    • Handle: RePEc:bas:econst:y:2022:i:1:p:139-159
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    References listed on IDEAS

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    4. Hafezali Iqbal Hussain & Beata Slusarczyk & Fakarudin Kamarudin & Hassanudin Mohd Thas Thaker & Katarzyna Szczepańska-Woszczyna, 2020. "An Investigation of an Adaptive Neuro-Fuzzy Inference System to Predict the Relationship among Energy Intensity, Globalization, and Financial Development in Major ASEAN Economies," Energies, MDPI, vol. 13(4), pages 1-17, February.
    5. Alexander Baranovsky & Nataliia Tkachenko & Vladimer Glonti & Valentyna Levchenko & Kateryna Bogatyrova & Zaza Beridze & Larisa Belinskaja & Iryna Zelenitsa, 2020. "Non-Price Criteria for the Evaluation of the Tender Offers in Public Procurement of Ukraine," IJFS, MDPI, vol. 8(3), pages 1-15, July.
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    8. Keshtkar, Azim & Arzanpour, Siamak, 2017. "An adaptive fuzzy logic system for residential energy management in smart grid environments," Applied Energy, Elsevier, vol. 186(P1), pages 68-81.
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    Cited by:

    1. Igor Britchenko & Oksana Polinkevych & Viktor Trynchuk & Inna Khovrak, 2023. "The Impact of COVID-19 on the Philosophy of Doing Business in a Sustainable Environment," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 100-116.

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    More about this item

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • O20 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy - - - General

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