A Spectral Index for Selecting the Best Alternative

This paper considers the problem of choosing the best design alternative under a small simulation budget where making inferences about all alternatives from a single observation could enhance the probability of correct selection. We propose a new selection rule exploiting the relative similarity information between pairs of alternatives and show its improvement on selection performance, evaluated by the probability of correct selection, compared to selection based on collected sample averages. We illustrate the effectiveness by applying our selection index on simulated ranking and selection problems using two well-known budget allocation policies.