This thesis presents the decoupled copula model, a novel theoretical framework for aggregating expert judgments in structured expert judgment (SEJ) studies. The model's key innovation lies in transforming expert assessments into a "decoupled space" where systematic biases can be identified and corrected while capturing potential inter-expert dependencies. Unlike existing Bayesian SEJ methods, which are limited to linear error metrics, our framework accommodates flexible dependency measures with rigorous theoretical criteria and practical tests for their evaluation. While previous Bayesian approaches acknowledged the possibility of bias correction, they lacked practical procedures for implementing these corrections using historical test data. Our framework addresses these limitations by enabling flexible metric choices for measuring biases and dependencies. Captured biases include under- and overconfidence as well as consistent over- and underestimation. Additionally, we introduce novel calibration criteria that have been proven necessary for perfect aggregation methods, along with interpretable calibration metrics that measure discrepancies from these criteria.
Empirical evaluation on 47 real-world SEJ studies demonstrates superior calibration properties while maintaining competitive predictive performance compared to established methods like the Classical Model. The empirical analysis reveals that inter-expert dependency modeling provides limited benefits, suggesting that systematic bias correction, rather than dependency modeling, drives improvements in aggregation performance in practical applications of our model.