Sequential Sampling to Myopically Maximize the Expected Value of Information

• Published in: INFORMS journal on computing Vol. 22; no. 1; pp. 71 - 80
• Main Authors: Chick, Stephen E, Branke, Jurgen, Schmidt, Christian
• Format: Journal Article
• Language: English
• Published: Linthicum INFORMS 01.01.2010; Institute for Operations Research and the Management Sciences
• Subjects: Algorithms; Approximation; Approximation theory; Asymptotic methods; Bayesian; Bayesian analysis; Bayesian statistical decision theory; decision analysis; Expected values; Information management; Methods; Sampling; sequential; simulation; statistical analysis; Statistical sampling; statistics; Studies; United States
• ISSN: 1091-9856; 1526-5528; 1091-9856
• DOI: 10.1287/ijoc.1090.0327

Statistical selection procedures are used to select the best of a finite set of alternatives, where "best" is defined in terms of each alternative's unknown expected value, and the expected values are inferred through statistical sampling. One effective approach, which is based on a Bayesian probability model for the unknown mean performance of each alternative, allocates samples based on maximizing an approximation to the expected value of information (EVI) from those samples. The approximations include asymptotic and probabilistic approximations. This paper derives sampling allocations that avoid most of those approximations to the EVI but entails sequential myopic sampling from a single alternative per stage of sampling. We demonstrate empirically that the benefits of reducing the number of approximations in the previous algorithms are typically outweighed by the deleterious effects of a sequential one-step myopic allocation when more than a few dozen samples are allocated. Theory clarifies the derivation of selection procedures that are based on the EVI.