New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
A Knowledge-Gradient Policy for Sequential Information Collection
SIAM Journal on Control and Optimization
The knowledge-gradient stopping rule for ranking and selection
Proceedings of the 40th Conference on Winter Simulation
Selecting a Selection Procedure
Management Science
Economic Analysis of Simulation Selection Problems
Management Science
Sequential Sampling to Myopically Maximize the Expected Value of Information
INFORMS Journal on Computing
Paradoxes in Learning and the Marginal Value of Information
Decision Analysis
Information Collection on a Graph
Operations Research
Sequential Sampling with Economics of Selection Procedures
Management Science
A Framework for Selecting a Selection Procedure
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Ranking and selection meets robust optimization
Proceedings of the Winter Simulation Conference
Value of information methods for pairwise sampling with correlations
Proceedings of the Winter Simulation Conference
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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.