The reconciliation of decision analyses
Operations Research
The reliability of subjective probabilities obtained through decomposition
Management Science
Assessing Dependence: Some Experimental Results
Management Science
eLearning Assessment through Textual Analysis of Class Discussions
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
IEEE Transactions on Software Engineering
Fuzzy knowledge-based model for soil condition assessment in Argentinean cropping systems
Environmental Modelling & Software
Decision Analysis
Decision Analysis
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Averaging forecasts from several experts has been shown to lead to improved forecasting accuracy and reduced risk of bad forecasts. Similarly, it is accepted knowledge in decision analysis that an expert can benefit from using more than one assessment method to look at a situation from different viewpoints. In this paper, we investigate gains in accuracy in assessing correlations by averaging different assessments from a single expert and/or from multiple experts. Adding experts and adding methods can both improve accuracy, with diminishing returns to extra experts or methods. The gains are generally much greater from adding experts than from adding methods, and restricting the set of experts to those who do particularly well individually leads to the greatest improvements in the averaged assessments. The variability of assessment accuracy decreases considerably as the number of experts or methods increases, implying a large risk reduction. We discuss conditions under which the general pattern of results obtained here might be expected to be similar or different in other situations with multiple experts and/or multiple methods.