Aggregating Probabilistic Judgments
This work addresses the challenge of combining probabilistic opinions in decision-making or AI systems, but it appears incremental as it builds on classical judgment aggregation methods.
The paper tackles the problem of aggregating probabilistic judgments on logically related issues by modifying the Boolean judgment aggregation framework to handle probabilities and defining generalized aggregation functions, while also discussing desirable properties and impossibility results.
In this paper we explore the application of methods for classical judgment aggregation in pooling probabilistic opinions on logically related issues. For this reason, we first modify the Boolean judgment aggregation framework in the way that allows handling probabilistic judgments and then define probabilistic aggregation functions obtained by generalization of the classical ones. In addition, we discuss essential desirable properties for the aggregation functions and explore impossibility results.