AN APPROXIMATE NONMYOPIC COMPUTATION FOR VALUE OF INFORMATION

Value-of-information analyses provide a means for selecting the next best observation to make and for determining whether it is better to gather additional information or to act immediately. Determining the next best test to perform. given uncertainty about the state of the world. requires a consideration of the value of making all possible sequences of observations. In practice, decision analysts and expert-system designers have avoided the intractability of exact computation of the value of information by relying on a myopic assumption that only one additional test will be performed, even when there is an opportunity to make a large number of observations. We present an alternative to the myopic analysis. In particular, we present an approximate method for computing the value of information of a set of tests, which exploits the statistical properties of large samples. The approximation is linear in the number of tests, in contrast with the exact computation, which is exponential in the number or tests. The approach is not as general as is a complete nonmyopic analysis, in which all possible sequences of observations are considered. In addition, the approximation is limited to specific classes of dependencies among evidence and to binary hypothesis and decision variables. Nonetheless, as we demonstrate with a simple application. the approach can offer an improvement over the myopic analysis.