Refined knowledge-gradient policy for learning probabilities

In a ranking and selection problem with independent Bernoulli populations we study the knowledge-gradient policy. We show that this policy is inefficient and propose three alternative rules that have superior performance. In particular we show that one of the considered policies–called KG(∗∗) in the literature–is numerically difficult and expensive to evaluate and it is possible to derive a much simpler policy that gives comparable results.