A Separating Choice Hyperplane Algorithm for Evaluating Multiattribute Decisions

This paper presents an iterative algorithm for optimizing multiattribute decisions. It develops a separating choice hyperplane algorithm, which allows decision-makers to identify their most preferred point from a nonlinear convex set of feasible outcomes. In this interactive algorithm, the decision-maker reveals information about preferences by solving a series of elementary resource allocations. The resulting structure integrates a theoretically rigorous optimization procedure with intuitive assessment tasks. A practical application of the approach is presented.