Implementing stochastic multicriteria acceptability analysis

Abstract Stochastic multicriteria acceptability analysis (SMAA) is a family of methods for aiding multicriteria group decision making in problems with inaccurate, uncertain, or missing information. These methods are based on exploring the weight space in order to describe the preferences that make each alternative the most preferred one, or that would give a certain rank for a specific alternative. The main results of the analysis are rank acceptability indices, central weight vectors and confidence factors for different alternatives. The rank acceptability indices describe the variety of different preferences resulting in a certain rank for an alternative, the central weight vectors represent the typical preferences favouring each alternative, and the confidence factors measure whether the criteria measurements are sufficiently accurate for making an informed decision. The computations in SMAA require the evaluation of multidimensional integrals that must in practice be computed numerically. In this paper we present efficient methods for performing the computations through Monte Carlo simulation, analyze the complexity, and assess the accuracy of the presented algorithms. We also test the efficiency of these methods empirically. Based on the tests, the implementation is fast enough to analyze typical-sized discrete problems interactively within seconds. Due to almost linear time complexity, the method is also suitable for analysing very large decision problems, for example, discrete approximations of continuous decision problems.