In this paper, we investigate the problem of eliciting new information, where the assumed uncertainty model are upper coherent previsions (or equivalently convex sets of probabilities), and where the elicitation goal is to solve, as quickly as possible, a decision problem where maximality is used as a decision rule. To address this question, we study the potential range of upper bounds an expert may give on a given query taking the form of an uncertain reward, providing new results and characterisation for such a range. We then use this range to provide an algorithm of elicitation. We illustrate our findings and proposal on an example.