Efficient Estimation of the Value of Information in Monte Carlo Models
  The expected value of information (EVI) is the most powerful measure of
sensitivity to uncertainty in a decision model: it measures the potential of
information to improve the decision, and hence measures the expected value of
outcome. Standard methods for computing EVI use discrete variables and are
computationally intractable for models that contain more than a few variables.
Monte Carlo simulation provides the basis for more tractable evaluation of
large predictive models with continuous and discrete variables, but so far
computation of EVI in a Monte Carlo setting also has appeared impractical. We
introduce an approximate approach based on pre-posterior analysis for
estimating EVI in Monte Carlo models. Our method uses a linear approximation to
the value function and multiple linear regression to estimate the linear model
from the samples. The approach is efficient and practical for extremely large
models. It allows easy estimation of EVI for perfect or partial information on
individual variables or on combinations of variables. We illustrate its
implementation within Demos (a decision modeling system), and its application
to a large model for crisis transportation planning.
