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Use of expert knowledge to anticipate the future: Issues, analysis and directions

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  • Bolger, Fergus
  • Wright, George

Abstract

Unless an anticipation problem is routine and short-term, and objective data are plentiful, expert judgment will be needed. Risk assessment is analogous to anticipating the future, in that models need to be developed and applied to data. Since objective data are often scanty, expert knowledge elicitation (EKE) techniques have been developed for risk assessment that allow models to be developed and parametrized using expert judgment with minimal cognitive and social biases. Here, we conceptualize how EKE can be developed and applied to support anticipation of the future. Accordingly, we begin by defining EKE as a complete process, which involves considering experts as a source of data, and comprises various methods for ensuring the quality of this data, including selecting the best experts, training experts in the normative aspects of anticipation, and combining judgments from several experts, as well as eliciting unbiased estimates and constructs from experts. We detail various aspects of the papers that constitute this special issue and analyse them in terms of the stages of the EKE future-anticipation process that they address. We also identify the remaining gaps in our knowledge. Our conceptualization of EKE with the aim of supporting anticipation of the future is compared and contrasted with the extant research on judgmental forecasting.

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  • Bolger, Fergus & Wright, George, 2017. "Use of expert knowledge to anticipate the future: Issues, analysis and directions," International Journal of Forecasting, Elsevier, vol. 33(1), pages 230-243.
    • Handle: RePEc:eee:intfor:v:33:y:2017:i:1:p:230-243
    DOI: 10.1016/j.ijforecast.2016.11.001
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    1. Chen, Kaihua & Ren, Zhipeng & Mu, Shijun & Sun, Tara Qian & Mu, Rongping, 2020. "Integrating the Delphi survey into scenario planning for China's renewable energy development strategy towards 2030," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    2. Bonaccorsi, Andrea & Apreda, Riccardo & Fantoni, Gualtiero, 2020. "Expert biases in technology foresight. Why they are a problem and how to mitigate them," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    3. Bolger, Fergus & Rowe, Gene & Belton, Ian & Crawford, Megan M & Hamlin, Iain & Sissons, Aileen & Taylor Browne Lūka, Courtney & Vasilichi, Alexandrina & Wright, George, 2020. "The Simulated Group Response Paradigm: A new approach to the study of opinion change in Delphi and other structured-group techniques," OSF Preprints 4ufzg, Center for Open Science.
    4. Mauksch, Stefanie & von der Gracht, Heiko A. & Gordon, Theodore J., 2020. "Who is an expert for foresight? A review of identification methods," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    5. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    6. Jiashun Huang & Weiping Li & Xijie Huang & Lijia Guo, 2017. "Analysis of the Relative Sustainability of Land Devoted to Bioenergy: Comparing Land-Use Alternatives in China," Sustainability, MDPI, vol. 9(5), pages 1-13, May.
    7. Kawamoto, Carlos Tadao & Wright, James Terence Coulter & Spers, Renata Giovinazzo & de Carvalho, Daniel Estima, 2019. "Can we make use of perception of questions' easiness in Delphi-like studies? Some results from an experiment with an alternative feedback," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 296-305.
    8. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Zhang, Huiling & Pang, Hongshen, 2021. "Multidimensional Scientometric indicators for the detection of emerging research topics," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    9. Legaki, Nikoletta-Zampeta & Karpouzis, Kostas & Assimakopoulos, Vassilios & Hamari, Juho, 2021. "Gamification to avoid cognitive biases: An experiment of gamifying a forecasting course," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    10. Apreda, Riccardo & Bonaccorsi, Andrea & dell'Orletta, Felice & Fantoni, Gualtiero, 2019. "Expert forecast and realized outcomes in technology foresight," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 277-288.
    11. Michael Pedersen, 2024. "Judgment in macroeconomic output growth predictions: Efficiency, accuracy and persistence," Papers 2404.04105, arXiv.org.
    12. Bonnin Roca, Jaime, 2022. "Teaching technological forecasting to undergraduate students: a reflection on challenges and opportunities," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    13. Abigail R Colson & Itamar Megiddo & Gerardo Alvarez-Uria & Sumanth Gandra & Tim Bedford & Alec Morton & Roger M Cooke & Ramanan Laxminarayan, 2019. "Quantifying uncertainty about future antimicrobial resistance: Comparing structured expert judgment and statistical forecasting methods," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-18, July.
    14. Xu, Shuo & Hao, Liyuan & Yang, Guancan & Lu, Kun & An, Xin, 2021. "A topic models based framework for detecting and forecasting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).

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