The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Use (and abuse) of expert elicitation in support of decision making for public policy
Open accessAbstract
The elicitation of scientific and technical judgments from experts, in the form of subjective probability distributions, can be a valuable addition to other forms of evidence in support of public policy decision making. This paper explores when it is sensible to perform such elicitation and how that can best be done. A number of key issues are discussed, including topics on which there are, and are not, experts who have knowledge that provides a basis for making informed predictive judgments; the inadequacy of only using qualitative uncertainty language; the role of cognitive heuristics and of overconfidence; the choice of experts; the development, refinement, and iterative testing of elicitation protocols that are designed to help experts to consider systematically all relevant knowledge when they make their judgments; the treatment of uncertainty about model functional form; diversity of expert opinion; and when it does or does not make sense to combine judgments from different experts. Although it may be tempting to view expert elicitation as a low-cost, low-effort alternative to conducting serious research and analysis, it is neither. Rather, expert elicitation should build on and use the best available research and analysis and be undertaken only when, given those, the state of knowledge will remain insufficient to support timely informed assessment and decision making.
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Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810
Judgment under Uncertainty: Heuristics and Biases
Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650
Judgment under Uncertainty: Heuristics and Biases
(1982) · doi:10.1017/cbo9780511809477 · Cited by 8,240
Van Der Sluijs, J. P., Craye, M., Funtowicz, S., Kloprogge, P., Ravetz, J., & Risbey, J. (2005) · Risk Analysis · doi:10.1111/j.1539-6924.2005.00604.x · Cited by 426
Cited by
The role of artificial intelligence in achieving the Sustainable Development Goals
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020) · Nature Communications · doi:10.1038/s41467-019-14108-y · Cited by 3,294
Climate as a risk factor for armed conflict
Mach, K. J., Kraan, C. M., Adger, W. N., Buhaug, H., Burke, M., Fearon, J. D., Field, C. B., Hendrix, C. S., Maystadt, J.-F., O’Loughlin, J., et al. (2019) · Nature · doi:10.1038/s41586-019-1300-6 · Cited by 619
A practical guide to structured expert elicitation using the IDEA protocol
Hemming, V., Burgman, M. A., Hanea, A. M., McBride, M. F., & Wintle, B. C. (2018) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12857 · Cited by 528
Battery cost forecasting: a review of methods and results with an outlook to 2050
Mauler, L., Duffner, F., Zeier, W. G., & Leker, J. (2021) · Energy & Environmental Science · doi:10.1039/d1ee01530c · Cited by 499
Expert elicitation survey predicts 37% to 49% declines in wind energy costs by 2050
Wiser, R., Rand, J., Seel, J., Beiter, P., Baker, E., Lantz, E., & Gilman, P. (2021) · Nature Energy · doi:10.1038/s41560-021-00810-z · Cited by 429
Metadata
- Authors
- Morgan, M. G.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2014-05-12
- Volume
- 111
- Issue
- 20
- Pages
- 7176-7184
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2111275873
- Cited by
- 702
- References
- 4
- FWCI
- 55.04
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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