  This paper makes the case for using Shapley value to quantify the importance
of random input variables to a function. Alternatives based on the ANOVA
decomposition can run into conceptual and computational problems when the input
variables are dependent. Our main goal here is to show that Shapley value
removes the conceptual problems. We do this with some simple examples where
Shapley value leads to intuitively reasonable nearly closed form values.
