Existing approaches to combining multiple forecasts generally offer either theoretical richness or empirical robustness, but not both together. In this paper, we propose a new method for combining forecasts which attempts to overcome this imbalance. The method allows easy inclusion of relevant subjective and empirical information about the forecasts, while providing weights which are: (i) intuitively meaningful, and (ii) not dependent upon large numbers of observations of prior forecast accuracy. Results of a simulation experiment show the method to be highly robust, and significantly superior to existing approaches under many conditions.