Table 1:
Terminology from analysis-set articles was collected and grouped by meaning. For each definition, the preferred terms is placed on top of all related terms. Definitions and preferred terminology were agreed upon by all coauthors.
| Related terms | Definition | Citations |
|---|---|---|
| Forecasting support system Adaptive management |
A framework for transforming data and forecasts into decisions. | (Fai, 2004; Son, 2013; Alv, 2017; Bae, 2017; Joh2018, 2018) |
| (Probabilistic) Safety Assessment (Probabilistic) Risk Assessment |
A framework for investigating the safety of a system |
Zio (1996), Zio (1997), Bor (2004), Cle (2007), Tar (2007), Kla (2010), Kur (2010), Bri (2012), Coo (2014a), Hat (2016), Mor (2017), Han (2018), Wan (2018), Jan (2019) |
| Information set Knowledge-base |
Data available to an expert, group of experts, or statistical model used for forecasting. | (Abr, 1996; Mak, 1996; Bor, 2004; Gra, 2014a; Bri, 2016; Alv, 2017) |
| Ill-structured tasks | When changes to an environment impact the probabilistic links between cues an expert receives and their effect (how these cues should should be interpreted). | (Sei, 2013; Hua, 2016) |
| Behavioral aggregation Behavioral combination Structured elicitation |
The support of expert discussion until they arrive at an agreed upon consensus distribution. | (Cle, 2007; Bri, 2012; Han, 2018) |
| Mathematical combination Mechanical integration |
The use of mathematical techniques to transform independent expert judgments into a single consensus distribution. | (Pet, 2006; Cle, 2007) |
| Judgmental adjustment Voluntary integration | Allowing experts to observe statistical forecasts, and provide their forecast as an adjustment to a present statistical forecast. | (Son, 2013; Hua, 2016; Alv, 2017; Bae, 2017) |
| Integrative judgment Knowledge-aggregation |
Forecasts from experts are incorporated into a forecasting model as a predictive variable. | (Mak, 1996; Bae, 2017) |
| Equal weighted 50–50 Weighting Unweighted |
Assigning equal weights to all experts in a combination method. | (Sar, 2013; Coo, 2014a; Gra, 2015; Alv, 2017; Han, 2018) |
| Nominal weights | Weights obtained by assessing experts performance on a set of calibration questions, or on observed data. | (Bal, 2015) |
| Cooke’s method Classical model |
Combining expert opinion via a linear pool where weights depend on expert’s answers to calibration questions with a known answer. | (Zio, 1996; Cle, 2007; Bri, 2012; Sar, 2013; Coo, 2014a; Hor, 2015; Hat, 2016; Bol, 2017; Mor, 2017; Han, 2018) |
| Mixed estimation Theil-Goldeberger mixed estimation |
A method for combining expert and statistical forecasts, stacking statistical and expert point predictions into a single vector and fitting a linear regression model. | (Alh, 1992; Shi, 2013) |
| Laplacian principle of indifference Principle of indifference |
In the context of expert combination, having no evidence related to expert forecasting performance, models should weight experts equally. | (Bol, 2017) |
| Brunswik lens model | A framework for relating a set of criteria (or indicators), expert’s judgment, and the ”correct” judgment. | (Fra, 2011; Sei, 2013) |