The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
A practical guide to structured expert elicitation using the IDEA protocol
Open accessAbstract
Expert judgement informs a variety of important applications in conservation and natural resource management, including threatened species management, environmental impact assessment and structured decision‐making. However, expert judgements can be prone to contextual biases. Structured elicitation protocols mitigate these biases, and improve the accuracy and transparency of the resulting judgements. Despite this, the elicitation of expert judgement within conservation and natural resource management remains largely informal. We suggest this may be attributed to financial and practical constraints, which are not addressed by many existing structured elicitation protocols. In this paper, we advocate that structured elicitation protocols must be adopted when expert judgements are used to inform science. In order to motivate a wider adoption of structured elicitation protocols, we outline the IDEA protocol. The protocol improves the accuracy of expert judgements and includes several key steps which may be familiar to many conservation researchers, such as the four‐step elicitation, and a modified Delphi procedure (“Investigate,” “Discuss,” “Estimate” and “Aggregate”). It can also incorporate remote elicitation, making structured expert judgement accessible on a modest budget. The IDEA protocol has recently been outlined in the scientific literature; however, a detailed description has been missing. This paper fills that important gap by clearly outlining each of the steps required to prepare for and undertake an elicitation. While this paper focuses on the need for the IDEA protocol within conservation and natural resource management, the protocol (and the advice contained in this paper) is applicable to a broad range of scientific domains, as evidenced by its application to biosecurity, engineering and political forecasting. By clearly outlining the IDEA protocol, we hope that structured protocols will be more widely understood and adopted, resulting in improved judgements and increased transparency when expert judgement is required.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Confirmation Bias: A Ubiquitous Phenomenon in Many Guises
Nickerson, R. S. (1998) · Review of General Psychology · doi:10.1037/1089-2680.2.2.175 · Cited by 7,267
On the psychology of prediction.
Kahneman, D., & Tversky, A. (1973) · Psychological Review · doi:10.1037/h0034747 · Cited by 6,202 · cited 1×
“This is often due to a range of contextual biases and heuristics such as anchoring, availability, and representativeness (Kahneman & Tversky, 1973), groupthink (Janis, 1971), overconfidence (Soll & Klayman, 2004) and difficulties associated with communicating knowledge in numbers and probabilities…”
Background(2004) · Choice Reviews Online · doi:10.5860/choice.42-1645 · Cited by 2,925
Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis
Morgan, M. G., & Henrion, M. (1990) · Cambridge University Press eBooks · doi:10.1017/cbo9780511840609 · Cited by 1,711
Uncertain Judgements: Eliciting Experts' Probabilities
O'Hagan, A., Buck, C. E., Daneshkhah, A., Eiser, J. R., Garthwaite, P. H., Jenkinson, D. J., Oakley, J. E., & Rakow, T. (2006) · doi:10.1002/0470033312 · Cited by 1,550
A literature review of the anchoring effect
Furnham, A., & Boo, H. C. (2011) · The Journal of Socio-Economics · doi:10.1016/j.socec.2010.10.008 · Cited by 1,179
Experts In Uncertainty: Opinion and Subjective Probability in Science
Cooke, R. M. (1991) · doi:10.1093/oso/9780195064650.001.0001 · Cited by 1,107
How social influence can undermine the wisdom of crowd effect
Lorenz, J., Rauhut, H., Schweitzer, F., & Helbing, D. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1008636108 · Cited by 1,017
Superforecasting: the art and science of prediction
(2016) · Choice Reviews Online · doi:10.5860/choice.196400 · Cited by 919
Cited by
How many bird and mammal extinctions has recent conservation action prevented?
Bolam, F. C., Mair, L., Angelico, M., Brooks, T. M., Burgman, M., Hermes, C., Hoffmann, M., Martin, R. W., McGowan, P. J. K., Rodrigues, A. S. L., et al. (2021) · Conservation Letters · doi:10.1111/conl.12762 · Cited by 286
Self-citationQuantifying Earth system interactions for sustainable food production via expert elicitation
Chrysafi, A., Virkki, V., Jalava, M., Sandström, V., Piipponen, J., Porkka, M., Lade, S. J., La Mere, K., Wang-Erlandsson, L., Scherer, L., et al. (2022) · Nature Sustainability · doi:10.1038/s41893-022-00940-6 · Cited by 41
Progress and opportunities in advancing near-term forecasting of freshwater quality
Lofton, M. E., Howard, D. W., Thomas, R. Q., & Carey, C. C. (2022) · doi:10.1002/essoar.10512426.1 · Cited by 38
Progress and opportunities in advancing near‐term forecasting of freshwater quality
(2023) · doi:10.1111/gcb.16590 · Cited by 38
Assessing causes and implications of climate-induced migration in Kenya and Ethiopia
Leal Filho, W., Ayal, D. Y., Chamma, D. D., Kovaleva, M., Nagle Alverio, G., Nzengya, D. M., Mucova, S. A. R., Kalungu, J. W., & Nagy, G. J. (2023) · Environmental Science & Policy · doi:10.1016/j.envsci.2023.103577 · Cited by 26
Metadata
- Authors
- Hemming, V., Burgman, M. A., Hanea, A. M., McBride, M. F., & Wintle, B. C.
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2017-09-05
- Volume
- 9
- Issue
- 1
- Pages
- 169-180
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W2741638098
- Cited by
- 528
- References
- 59
- FWCI
- 59.40
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A practical guide to structured expert elicitation using the IDEA protocol. https://app.cochat.ai/papers/10.1111/2041-210x.12857
- The paper
- Hemming, V., Burgman, M. A., Hanea, A. M., McBride, M. F., & Wintle, B. C. (2018). A practical guide to structured expert elicitation using the IDEA protocol. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.12857
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.