Generalizing the conjunction rule for aggregating conflicting expert opinions

Abstract

In multiagent expert systems, the conjunction rule is commonly used to combine expert information represented by imprecise probabilities. However, it is well known that this rule cannot be applied in the case of expert conflict. In this article, we propose to resolve expert conflict by means of a second-order imprecise probability model. The essential idea underlying the model is a notion of behavioral trust. We construct a simple linear programming algorithm for calculating the aggregate. This algorithm explains the proposed aggregation method as a generalized conjunction rule. It also provides an elegant operational interpretation of the imprecise second-order assessments, and thus overcomes the problems of interpretation that are so common in hierarchical uncertainty models.