Efficient non-myopic value-of-information computation for influence diagrams

In an influence diagram (ID), value-of-information (VOI) is defined as the difference between the maximum expected utilities with and without knowing the outcome of an uncertainty variable prior to making a decision. It is widely used as a sensitivity analysis technique to rate the usefulness of various information sources, and to decide whether pieces of evidence are worth acquisition before actually using them. However, due to the exponential time complexity of exactly computing VOI of multiple information sources, decision analysts and expert-system designers focus on the myopic VOI, which assumes observing only one information source, even though several information sources are available. In this paper, we present an approximate algorithm to compute non-myopic VOI efficiently by utilizing the central-limit theorem. The proposed method overcomes several limitations in the existing work. In addition, a partitioning procedure based on the d-separation concept is proposed to further improve the computational complexity of the proposed algorithm. Both the experiments with synthetic data and the experiments with real data from a real-world application demonstrate that the proposed algorithm can approximate the true non-myopic VOI well even with a small number of observations. The accuracy and efficiency of the algorithm makes it feasible in various applications where efficiently evaluating a large amount of information sources is necessary.