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Cited by 24Forecasting Techniques and ApplicationsInnovation Diffusion and ForecastingDelphi Technique in Research

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A strategy to improve expert technology forecasts

Open access
Savage, Tamara, Davis, Alex, Fischhoff, Baruch, Morgan, M. Granger·2021·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.2021558118PMC PMC8166153
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Abstract

Forecasts of the future cost and performance of technologies are often used to support decision-making. However, retrospective reviews find that many forecasts made by experts are not very accurate and are often seriously overconfident, with realized values too frequently falling outside of forecasted ranges. Here, we outline a hybrid approach to expert elicitation that we believe might improve forecasts of future technologies. The proposed approach iteratively combines the judgments of technical domain experts with those of experts who are knowledgeable about broader issues of technology adoption and public policy. We motivate the approach with results from a pilot study designed to help forecasters think systematically about factors beyond the technology itself that may shape its future, such as policy, economic, and social factors. Forecasters who received briefings on these topics provided wider forecast intervals than those receiving no assistance.

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  • Forecasting is difficult in general, and subject-matter experts have been observed to perform poorly.

    “Forecasting is difficult in general, and subject-matter experts have been observed to perform poorly [Tetlock, 2005, Savage et al., 2021].”

    From Thousands of AI Authors on the Future of AI (2025) · cites this paper as Savage et al., 2021

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Metadata

Authors
Savage, T., Davis, A., Fischhoff, B., & Morgan, M. G.
Affiliations
Carnegie Mellon University (US)
Corresponding author
Morgan, M. Granger
ORCID
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
2021-05-14
Volume
118
Issue
21
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.2021558118
PMC
PMC8166153
OpenAlex
W3161883901
Cited by
24
References
2
Citations by year
2026: 3 · 2025: 6 · 2024: 3 · 2023: 7 · 2021: 4
FWCI
1.94
Type
journal article
Language
en
Open access
Licence
www.pnas.org/site/aboutpnas/licenses.xhtml (vor, 184 d)
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Sloan Foundation (G-2020-12616)
  • NSF (SES-0949710)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Innovation Diffusion and Forecasting
  • Social Sciences › Sociology and Political Science › Delphi Technique in Research
Keywords
expert forecasting, technology forecasting, expert elicitation, hybrid forecasting, forecast accuracy, overconfidence, forecast intervals, technology adoption, public policy, socioeconomic factors, decision support

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