Simulation Modeling and Analysis
Simulation Modeling and Analysis
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
A large deviations perspective on ordinal optimization
WSC '04 Proceedings of the 36th conference on Winter simulation
Selection Procedures with Frequentist Expected Opportunity Cost Bounds
Operations Research
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
A Knowledge-Gradient Policy for Sequential Information Collection
SIAM Journal on Control and Optimization
Selecting a Selection Procedure
Management Science
Economic Analysis of Simulation Selection Problems
Management Science
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Paradoxes in Learning and the Marginal Value of Information
Decision Analysis
Information Collection on a Graph
Operations Research
The conjunction of the knowledge gradient and the economic approach to simulation selection
Winter Simulation Conference
Winter Simulation Conference
The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery
INFORMS Journal on Computing
Hierarchical Knowledge Gradient for Sequential Sampling
The Journal of Machine Learning Research
Sequential Sampling with Economics of Selection Procedures
Management Science
The Knowledge Gradient Algorithm for a General Class of Online Learning Problems
Operations Research
Optimization via simulation with Bayesian statistics and dynamic programming
Proceedings of the Winter Simulation Conference
Ranking and selection with unknown correlation structures
Proceedings of the Winter Simulation Conference
Ranking and selection meets robust optimization
Proceedings of the Winter Simulation Conference
Sequential screening: a Bayesian dynamic programming analysis of optimal group-splitting
Proceedings of the Winter Simulation Conference
Optimal computing budget allocation for small computing budgets
Proceedings of the Winter Simulation Conference
Guessing preferences: a new approach to multi-attribute ranking and selection
Proceedings of the Winter Simulation Conference
Optimal learning of transition probabilities in the two-agent newsvendor problem
Proceedings of the Winter Simulation Conference
Rapid Screening Procedures for Zero-One Optimization via Simulation
INFORMS Journal on Computing
Efficiently gathering information in costly domains
Decision Support Systems
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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.