The nature of much information available to decision makers is vague and imprecise, be it information for human managers in organisations or for process agents in a distributed computer environment. Several models for handling vague and imprecise information in decision situations have been suggested. In particular, various interval methods have prevailed, i.e. methods based on interval estimates of probabilities and, in some cases, interval utility estimates. Even if these approaches in general are well founded, little has been done to take into consideration the evaluation perspective and, in particular, computational aspects and implementation issues. The purpose of this paper is to demonstrate a tool for handling imprecise information in decision situations. The tool is an implementation of our earlier research focussing on finding fast algorithms for solving bilinear systems of equations together with a graphical user interface supporting the interpretation of evaluations of imprecise data.

This paper present a method for inclusion of influence diagrams within a common framework for analysing decisions under risk supporting interval-valued user statements. The method of inclusion support both modeling and evaluation, and the evaluation is performed through a conversion of influence diagrams into decision tree frames holding symmetric decision trees. The qualitative and explicit modeling of probabilistic independence cause implicit comparative constraints between variables, constraints which must not be violated in order to obtain correct evaluation results.

We present a decision tree evaluation method for analyzing multi-attribute decisions under risk, where information is numerically imprecise. The approach extends the use of additive and multiplicative utility functions for supporting evaluation of imprecise statements, relaxing requirements for precise estimates of decision parameters. Information is modeled in convex sets of utility and probability measures restricted by closed intervals. Evaluation is done relative to a set of rules, generalizing the concept of admissibility, computationally handled through optimization of aggregated utility functions. Pros and cons of two approaches, and tradeoffs in selecting a utility function, are discussed.

We present a case study in which a decision support method (ADL) was employed by a local government in order to guide and aid decisions on three complicated and politically infected issues which had remained unresolved for many years. The research inquiry was whether a well-defined and openly accessible method would aid a common understanding of the decision problems, and whether people would be able to accept a clearly motivated decision even if politically they preferred a different option. The ADL method has been used in several public sector projects ranging from very large purchasing decisions to the selection of national policies, but this test case was novel in that it involved close inspection by the public. This case was also devised as a test of new methods for potential inclusion into normal practices. The post-case analysis shows mixed understanding of and belief in the method. The results raise issues concerning both the potential for decision support methods in a political context and the nature of political decision making.