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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. Eden, Colin, 1988. "Cognitive mapping," European Journal of Operational Research, Elsevier, vol. 36(1), pages 1-13, July.
    2. Wilson, Kevin J., 2017. "An investigation of dependence in expert judgement studies with multiple experts," International Journal of Forecasting, Elsevier, vol. 33(1), pages 325-336.
    3. Willy Aspinall, 2010. "A route to more tractable expert advice," Nature, Nature, vol. 463(7279), pages 294-295, January.
    4. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    5. Ronald A. Howard & James E. Matheson, 2005. "Influence Diagrams," Decision Analysis, INFORMS, vol. 2(3), pages 127-143, September.
    6. Eggstaff, Justin W. & Mazzuchi, Thomas A. & Sarkani, Shahram, 2014. "The effect of the number of seed variables on the performance of Cooke′s classical model," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 72-82.
    7. Petropoulos, Fotios & Goodwin, Paul & Fildes, Robert, 2017. "Using a rolling training approach to improve judgmental extrapolations elicited from forecasters with technical knowledge," International Journal of Forecasting, Elsevier, vol. 33(1), pages 314-324.
    8. Cooke, Roger M. & ElSaadany, Susie & Huang, Xinzheng, 2008. "On the performance of social network and likelihood-based expert weighting schemes," Reliability Engineering and System Safety, Elsevier, vol. 93(5), pages 745-756.
    9. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
    10. Meissner, Philip & Brands, Christian & Wulf, Torsten, 2017. "Quantifiying blind spots and weak signals in executive judgment: A structured integration of expert judgment into the scenario development process," International Journal of Forecasting, Elsevier, vol. 33(1), pages 244-253.
    11. Schoemaker, Paul J.H. & Day, George S. & Snyder, Scott A., 2013. "Integrating organizational networks, weak signals, strategic radars and scenario planning," Technological Forecasting and Social Change, Elsevier, vol. 80(4), pages 815-824.
    12. Keren, Gideon, 1987. "Facing uncertainty in the game of bridge: A calibration study," Organizational Behavior and Human Decision Processes, Elsevier, vol. 39(1), pages 98-114, February.
    13. Hanea, A.M. & McBride, M.F. & Burgman, M.A. & Wintle, B.C. & Fidler, F. & Flander, L. & Twardy, C.R. & Manning, B. & Mascaro, S., 2017. "I nvestigate D iscuss E stimate A ggregate for structured expert judgement," International Journal of Forecasting, Elsevier, vol. 33(1), pages 267-279.
    14. Rowe, Gene & Wright, George, 1999. "The Delphi technique as a forecasting tool: issues and analysis," International Journal of Forecasting, Elsevier, vol. 15(4), pages 353-375, October.
    15. Lin, Shi-Woei & Bier, Vicki M., 2008. "A study of expert overconfidence," Reliability Engineering and System Safety, Elsevier, vol. 93(5), pages 711-721.
    16. Derbyshire, James & Wright, George, 2017. "Augmenting the intuitive logics scenario planning method for a more comprehensive analysis of causation," International Journal of Forecasting, Elsevier, vol. 33(1), pages 254-266.
    17. Franses, Philip Hans & Kranendonk, Henk C. & Lanser, Debby, 2011. "One model and various experts: Evaluating Dutch macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 482-495.
    18. Ronald A. Howard & James E. Matheson, 2005. "Influence Diagram Retrospective," Decision Analysis, INFORMS, vol. 2(3), pages 144-147, September.
    19. Mark A Burgman & Marissa McBride & Raquel Ashton & Andrew Speirs-Bridge & Louisa Flander & Bonnie Wintle & Fiona Fidler & Libby Rumpff & Charles Twardy, 2011. "Expert Status and Performance," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-7, July.
    20. Alvarado-Valencia, Jorge & Barrero, Lope H. & Önkal, Dilek & Dennerlein, Jack T., 2017. "Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting," International Journal of Forecasting, Elsevier, vol. 33(1), pages 298-313.
    21. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
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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. 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).
    12. 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.
    13. 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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