An Approximate Nonmyopic Computation for Value of Information

David Heckerman, Eric Horvitz, Blackford Middleton

Medical Computer Science Group
Knowledge Systems Laboratory
Stanford University
Stanford, California 94305

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Value-of-information analyses provide a straightforward means for selecting the best next 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 a state of uncertainty about 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 approximation. Myopic analyses are based on the assumption that only one additional test will be performed, even when there is an opportunity to make a large number of observations. We present a nonmyopic approximation for value of information that bypasses the traditional myopic analyses by exploiting the statistical properties of large samples.

Keywords: Expected value of information, probabilistic reasoning, normaitive diagnosis.

  • D. Heckerman, E. Horvitz, and B. Middleton, An approximate nonmyopic computation for value of information, IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 15 (1993), 3:292-298.