The effect of the number of seed variables on the performance of Cooke′s classical model

In risk analysis, Cooke′s classical model for aggregating expert judgment has been widely used for over 20 years. However, the validity of this model has been the subject of much debate. Critics assert that this model′s scoring rule may unintentionally reward experts who manipulate their quantile estimates in order to receive a greater weight. In addition, the question of the number of seed variables required to ensure adequate performance of Cooke′s classical model remains unanswered. In this study, we conduct a comprehensive examination of the model through an iterative, cross validation test to perform an out-of-sample comparison between Cooke′s classical model and the equal-weight linear opinion pool method on almost all of the expert judgment studies compiled by Cooke and colleagues to date. Our results indicate that Cooke′s classical model significantly outperforms equally weighting expert judgment, regardless of the number of seed variables used; however, there may, in fact, be a maximum number of seed variables beyond which Cooke′s model cannot outperform an equally-weighted panel.