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 paper, 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.
Modeling the behavior of complex systems that operate in real environments, deal with physical elements, or interact with humans is a challenging task. It involves the explicit representation of aspects of behavioral uncertainty that are inherent in the system, but generally neglected in software models. In this paper, we focus on the explicit representation of the behavior of objects of complex systems, considering their motivations, randomness, and the different types of underlying uncertainty that affect their actions. We show how such uncertain behaviors can be effectively modeled in UML and OCL, and how the specifications produced can be used to simulate and analyze these systems.
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