This thesis falls within the area of algorithmic decision theory, which is at the crossroads between decision theory, operational research and artificial intelligence. In this thesis, we study several decision models to solve problems in different domains: sequential decision problems under risk, robust optimization problems, and fair multi-agent optimization problems. To solve these problems efficiently, we use master-slave algorithms which solve the problem through an incremental process. These procedures, referred to as oracle methods in the thesis, make it possible to solve problems of large size. A particular attention is given to the skew-symmetric bilinear utility model, the weighted expected utility model and their counterparts in multicriteria decision making. These models are interesting at several respects. They extend the standard models (e.g., the expected utility model) and allow to represent a broader class of preferences while retaining their good theoretical and algorithmic properties. The thesis focuses both on theoretic (e.g., complexity results) and operational (e.g., design of practically efficient solution methods) aspects of the problems raised by the use of these criteria in the domains aforementioned.?