The conjunction of the knowledge gradient and the economic approach to simulation selection

This paper deals with the selection of the best of a finite set of systems, where best is defined with respect to the maximum mean simulated performance. We extend the ideas of the knowledge gradient, which accounts for the expected value of one stage of simulation, by accounting for the future value of the option to simulate over multiple stages. We extend recent work on the economics of simulation, which studied discounted rewards, by balancing undiscounted simulation costs and the expected value of information from simulation runs. This contribution results in a diffusion model for comparing a single simulated system with a standard that has a known expected reward, and new stopping rules for fully sequential procedures when there are multiple systems. These stopping rules are more closely aligned with the expected opportunity cost allocations that are effective in numerical tests. We demonstrate an improvement in performance over previous methods.