Summary: Existing ordered weighted average (OWA) characterization methods maximize similarity among information sources by seeking maximal weights entropy or by minimizing weights variance. These methods are based solely on the weights, and the uncertainties of input information sources are ignored. However, the purpose of information fusion is to decrease uncertainty and improve data quality. Following this objective, this work proposes a new method to calculate the OWA weights based on the minimization of the aggregated uncertainty. The resulting aggregated value is the most precise, in the sense that any other combination of weights produces larger uncertainty.