Dealing with Belief Uncertainty in Domain Models

There are numerous domains in which information systems need to deal with uncertain information. These uncertainties may originate from different reasons such as vagueness, imprecision, incompleteness, or inconsistencies, and in many cases, they cannot be neglected. In this article, we are interested in representing and processing uncertain information in domain models, considering the stakeholders' beliefs (opinions). We show how to associate beliefs to model elements and how to propagate and operate with their associated uncertainty so that domain experts can individually reason about their models enriched with their personal opinions. In addition, we address the challenge of combining the opinions of different domain experts on the same model elements, with the goal to come up with informed collective decisions. We provide different strategies and a methodology to optimally merge individual opinions.

Automated summary

The article explores the handling of uncertain information in various domains of information systems. It focuses on representing and processing uncertain information in domain models and incorporating stakeholders' beliefs. The authors demonstrate the association of beliefs with model elements, the propagation and operation of associated uncertainty, and the individual reasoning about models enriched with personal opinions. Additionally, the article addresses the challenge of merging opinions from different domain experts and offers strategies and a methodology for optimal opinion merging.