Machine learning algorithms dedicated to financial time series forecasting have gained a lot of interest. But choosing between several algorithms can be challenging, as their estimation accuracy may be unstable over time. Online aggregation of experts combine the forecasts of a finite set of...

Research has shown that aggregation of independent expert judgments significantly improves the quality of forecasts as compared to individual expert forecasts. This “wisdom of crowds” (WOC) has sparked substantial interest. However, previous studies on strengths and weaknesses of aggregation...

How can a decision-maker assess the potential of environmental policies when a group of experts provides divergent estimates on their effectiveness? To address this question, we propose and analyze a variant of the well-studied $\alpha$-maxmin model in decision theory. In our framework, and...

Purpose – Methods and techniques of aggregating preferences or priorities in the analytic hierarchy process (AHP) usually ignore variation or dispersion among experts and are vulnerable to extreme values (generated by particular viewpoints or experts trying to distort the final ranking). The...

Purpose – The purpose of this paper is to investigate how expert overconfidence and dependence affect the calibration of aggregated probability judgments obtained by various linear opinion‐pooling models. Design/methodology/approach – The authors used a large database containing...