A neural network approach to decision alternative prioritization

Abstract A common decision problem faced by managers in organizations is that of decision alternative prioritization. There have been many proposed approaches to the problem where the decision maker constructs a pairwise comparison matrix of the alternatives under study. All existing ranking methods possess major shortcomings for the general problem. This paper illustrates the usefulness of a neural network model in such prioritization problems, which considers these shortcomings of previous methods. Use of the model is shown through the use of example ranking scenarios.