Recently, representations and methods analysing decision problems where probabilities and values (utilities) are associated with belief distributions over them (second order representations) have been suggested. Inthis paper we present an approach to how imprecise information can be modelled by means of second-order distri-butions and how a risk evaluation process can be elaborated by integrating procedures for numerically impreciseprobabilities and utilities. We discuss some shortcomings in the use of the principle of maximising the expectedutility and of utility theory in general, and offer remedies by the introduction of supplementary decision rules basedon a concept of risk constraints taking advantage of second-order distributions.