The knowledge-gradient stopping rule for ranking and selection
Proceedings of the 40th Conference on Winter Simulation
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
Simulation model calibration with correlated knowledge-gradients
Winter Simulation Conference
The conjunction of the knowledge gradient and the economic approach to simulation selection
Winter Simulation Conference
Winter Simulation Conference
Efficient Risk Estimation via Nested Sequential Simulation
Management Science
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
Consistency of Sequential Bayesian Sampling Policies
SIAM Journal on Control and Optimization
Sequential Sampling with Economics of Selection Procedures
Management Science
Sequential design of computer experiments for the estimation of a probability of failure
Statistics and Computing
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 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
Value of information methods for pairwise sampling with correlations
Proceedings of the Winter Simulation Conference
May the best man win: simulation optimization for match-making in e-sports
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
Optimal learning for sequential sampling with non-parametric beliefs
Journal of Global Optimization
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In a sequential Bayesian ranking and selection problem with independent normal populations and common known variance, we study a previously introduced measurement policy which we refer to as the knowledge-gradient policy. This policy myopically maximizes the expected increment in the value of information in each time period, where the value is measured according to the terminal utility function. We show that the knowledge-gradient policy is optimal both when the horizon is a single time period and in the limit as the horizon extends to infinity. We show furthermore that, in some special cases, the knowledge-gradient policy is optimal regardless of the length of any given fixed total sampling horizon. We bound the knowledge-gradient policy's suboptimality in the remaining cases, and show through simulations that it performs competitively with or significantly better than other policies.