  In the context of multicriteria decision making, the ordered weighted
averaging (OWA) functions play a crucial role in aggregating multiple criteria
evaluations into an overall assessment supporting the decision makers' choice.
Determining OWA weights, therefore, is an essential part of this process.
Available methods for determining OWA weights, however, often require heavy
computational loads in real-life large-scale optimization problems. In this
paper, we propose a new approach to simplify the well-known minimax disparity
model for determining OWA weights. For this purpose, we use to the binomial
decomposition framework in which natural constraints can be imposed on the
level of complexity of the weight distribution. The original problem of
determining OWA weights is thereby transformed into a smaller scale
optimization problem, formulated in terms of the coefficients in the binomial
decomposition. Our preliminary results show that a small set of these
coefficients can encode for an appropriate full-dimensional set of OWA weights.
