The initial draft of this article appeared as a School of Business Working Paper No. 273, dated January 1996, and titled "A Forward Monte Carlo Method For Solving Influence Diagrams Using Local Computation." A short version of this Working Paper appeared as "A Forward Monte Carlo Method for Solving Influence Diagrams Using Local Computation," Preliminary Papers of the Sixth International Workshop on Artificial Intelligence and Statistics, pp. 75--82, January 1997.The main goal of this paper is to describe a new multistage Monte Carlo (MMC) simulation method for solving influence diagrams using local computation. Global methods have been proposed by others that sample from the joint probability distribution of all the variables in the influence diagram. However, for influence diagrams having many variables, the state space of all variables grows exponentially, and the sample sizes required for good estimates may be too large to be practical. In this paper, we develop a MMC method, which samples only a small set of chance variables for each decision node in the influence diagram. MMC is akin to methods developed for exact solution of influence diagrams in that we limit the number of chance variables sampled at any time. Because influence diagrams model each chance variable with a conditional probability distribution, the MMC method lends itself well to influence diagram representations.Partially supported by a grant from Sprint and Nortel Networks to John M. Charnes and by a contract from Sparta, Inc., to Prakash P. Shenoy.