Imprecise information in multi-level decision trees

Abstract: The information available to decision makers is often vague and imprecise, and various methods basedon interval estimates of probabilities and utilities have been proposed to deal with this. The discussionhas, however, mostly evolved around representation, and much less has been done to take intoconsideration the evaluation, and also computational and implementation aspects has been left out.The Delta method for handling vague and imprecise information is one of the most elaboratedapproaches in its category and is therefore a reasonable starting point for this thesis. However, onemajor disadvantage is that the approach only handles single-level decision trees and cannot nontriviallybe extended to handle multi-level trees. The capability of handling multi-level trees isimportant, since it appears naturally in many real-life situations.The purpose of this thesis is to present a generalization allowing for multi-level trees and impreciseinformation, thus extending the Delta approach. The extension is implemented in the decision softwareDecideIT, which consequently allows for interval statements and value comparisons between differentconsequences, in the form of multi-level trees. Five papers are attached to the thesis. Two of thesepresent the necessary algorithms and an implementation employing them. The third and fourth papersdemonstrate how decision problems can be modelled and evaluated taking into account the impreciseinput data. A fifth paper presents how the method can be extended to a multi-attribute decision treeevaluation method.