Semimyopic Measurement Selection for Optimization Under Uncertainty

Abstract

The following sequential decision problem is considered: given a set of items of unknown utility, an item with as high a utility as possible must be selected ("the selection problem"). Measurements (possibly noisy) of item features prior to selection are allowed at known costs. The goal is to optimize the overall sequential decision process of measurements and selection. Value of information (VOI) is a well-known scheme for selecting measurements, but the intractability of the problem typically leads to using myopic VOI estimates. In the selection problem, myopic VOI frequently badly underestimates the VOI, leading to inferior measurement policies. In this paper, the strict myopic assumption is relaxed into a scheme termed semimyopic, providing a spectrum of methods that can improve the performance of measurement policies. In particular, the efficiently computable method of "blinkered" VOI is proposed, and theoretical bounds for important special cases are examined. Empirical evaluation of "blinkered" VOI in the selection problem with normally distributed item values shows that it performs much better than pure myopic VOI.