A streamlined approach for calculating expected utility and expected value of perfect information

An approach based on the criteria of maximizing expected utility was developed for comparing alternatives, and the expected value of perfect information was incorporated to indicate the relative importance of uncertainty. Multi-dimensional Gauss quadrature was used to integrate over the uncertain variables. Problem characteristics were taken into account to subdivide utility functions, reducing the amount of computation when integrating utility. Additional factors were included to streamline the calculation of expected value of perfect information. Problems were taken from the literature to test the convergence rate of the method. The quadrature results converged quickly, although the rate depended on the type of probability distributions assigned to the variables.