We review the reasoning underlying two approaches to combination of sensory uncertainties. First approach is noncommittal, making no assumptions about properties of uncertainty or parameters of stimulation. Then we explain the relationship between this approach and the one commonly used in modeling "higher level" aspects of sensory systems, such as in visual cue integration, where assumptions are made about properties of stimulation. The two approaches follow similar logic, except in one case maximal uncertainty is minimized, and in the other minimal certainty is maximized. Then we demonstrate how optimal solutions are found to the problem of resource allocation under uncertainty.