Group elicitations yield more consistent, yet more uncertain experts in understanding risks to ecosystem services in New Zealand bays
Bibliographic record
Abstract
The elicitation of expert judgment is an important tool for assessment of risks and impacts in environmental management contexts, and especially important as decision-makers face novel challenges where prior empirical research is lacking or insufficient. Evidence-driven elicitation approaches typically involve techniques to derive more accurate probability distributions under fairly specific contexts. Experts are, however, prone to overconfidence in their judgements. Group elicitations with diverse experts can reduce expert overconfidence by allowing cross-examination and reassessment of prior judgements, but groups are also prone to uncritical "groupthink" errors. When the problem context is underspecified the probability that experts commit groupthink errors may increase. This study addresses how structured workshops affect expert variability among and certainty within responses in a New Zealand case study. We find that experts' risk estimates before and after a workshop differ, and that group elicitations provided greater consistency of estimates, yet also greater uncertainty among experts, when addressing prominent impacts to four different ecosystem services in coastal New Zealand. After group workshops, experts provided more consistent ranking of risks and more consistent best estimates of impact through increased clarity in terminology and dampening of extreme positions, yet probability distributions for impacts widened. The results from this case study suggest that group elicitations have favorable consequences for the quality and uncertainty of risk judgments within and across experts, making group elicitation techniques invaluable tools in contexts of limited data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".