Uncertainty is an essential aspect of risk assessments that should be accounted for in the analysis considering potential implications for the decision-making process. Uncertainty depends in particular on the information at hand regarding factors that influence risk. It is suggested in this report that different types of information, regarding variables that appear in risk assessment models, re...

Expert knowledge is an important source of input to risk analysis. In practice, experts might be reluctant to characterize their knowledge and the related (epistemic) uncertainty using precise probabilities. The theory of possibility allows for imprecision in probability assignments. The associated possibilistic representation of epistemic uncertainty can be combined with, and transformed into,...

Complex hybrid systems can have several sources of uncertainty. Uncontrollable events that change the state of the system and can be triggered unpredictably or adversarially are typically modelled as nondeterministic whereas events to which we can attribute some likelihood, either because of their fundamental nature or because of simplifying assumptions, are typically modelled as stochastic. Al...

Probabilistic and possibilistic models of sequential decision problems are known to possess good behavioral algorithmic properties. In this paper, the range under uncertainty that dynamically consistent, consequentialist allow for tree reduction is enlarged by considering a representation both probabilistic possibilistic. The corresponding utility functional expected highly likely states, an op...

Nowadays, dust phenomenon is one of the important challenges in warm and dry areas. Forecasting the phenomenon before its occurrence helps to take precautionary steps to prevent its consequences. Fuzzy expert systems capabilities have been taken into account to assist and cope with the uncertainty associated to complex environments such as dust forecasting problem. This paper presents novel hyb...