Representing Human Uncertainty by Subjective Likelihood Estimates

Abstract

We give a definition of human uncertainty through subjective likelihood estimates. The subject is asked for his estimated likelihood of a discrete variable, given a present piece of uncertain observation, under the hypothetical assumption that the variable was uniformly distributed prior to the new observation. With this interpretation of human uncertainty, we are able to perform consistent inference about our target variable, by formally treating the input as likelihood factors. The algorithm has been successfully implemented in an expert system for classification of wildwood mushrooms.