Use (and abuse) of expert elicitation in support of decision making for public policy

By M. Granger Morgan. Proceedings of the National Academy of Sciences of the United States of America, 2014

628 citations recorded by the source corpus.

Subfields: General Decision Sciences, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/4469d3104d630107c9bf505fa614320ecce9e86d

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Citations in the corpus

A practical guide to structured expert elicitation using the IDEA protocol (2018, methodology): “…treat the elicitation of expert judgements in the same regard as empirical data, by using repeatable, transparent methods and addressing scientific questions (not value judgements) in the form of probabilities and quantities (Aspinall, 2010; Aspinall & Cooke, 2013; French, 2011; Morgan, 2014).”
Quantifying extinction risk and forecasting the number of impending Australian bird and mammal extinctions (2018, neutral): “While the known biases in elicitation methods can only partially be overcome (Morgan 2014; Montibeller and Winterfeldt 2015), using multiple experts to assess the probability of extinction independently, discussing discrepancies among assessors, and reconciling inconsistencies and differences in…”
The role of artificial intelligence in achieving the Sustainable Development Goals (2019, methodology): “To this end, we conducted a consensus-based expert elicitation process, informed by previous studies on mapping SDGs interlinkages 8,9 and following Butler et al. 67 and Morgan 68 .”
Expert elicitation survey on future wind energy costs (2016, methodology): “Expert Elicitation Survey Expert elicitation is a tool used to develop estimates of unknown or uncertain quantities based on careful assessment of the knowledge and beliefs of experts about those quantities [33].”
Biomass offsets little or none of permafrost carbon release from soils, streams, and wildfire: an expert assessment (2016, methodology): “While analysis of best estimates can return narrower uncertainty ranges than subjective probability distributions (Morgan 2014), we believe this tradeoff resulted in broader expert participation, better representing diversity of opinion across disciplines and compensating for possible…”
Use of expert knowledge to elicit population trends for the koala (Phascolarctos cinereus) (2016, background): “For example, opinions may differ on the basis of different interpretations of data (Morgan, 2014).”
Mental models for conservation research and practice (2019, background): “Data, typically “scientific evidence,” are necessary to develop these relationships but are often missing or incomplete and must, therefore, be complemented with expert elicitation of mental models of system relationships (Colvin et al., 2016; Morgan, 2014).”
Assessing changes in global fire regimes (2023, background): “Combining assessments from multiple sources allows an integrative evaluation of the range of possible futures complementary to numerical model projections (Morgan, 2014; Sayedi et al., 2020; Schuur et al., 2013).”
The social cost of carbon revisited (2016, positive): “Morgan (2014) has argued that it is preferable to move from probabilities of extreme outcomes to less extreme ones, but when testing the questionnaire, I found that people then had more trouble thinking about probabilities.”
Efficiently encoding and modeling subjective probability distributions for quantitative variables. (2016, background): “Morgan (2014) provides an excellent discussion of the issues and difficulties involved in eliciting full probability distributions from experts.”
When Risks Cannot Be Seen: Regulating Uncertainty in Emerging Technologies (2017, neutral): “One option for policymakers to assess uncertainty in emerging technologies could be expert elicitation, but results are subject to overconfidence and in general do not take into account variables which go beyond well-established knowledge (Morgan, 2014).”