A gradient approach for smartly allocating computing budget for discrete event simulation
WSC '96 Proceedings of the 28th conference on Winter simulation
New development of optimal computing budget allocation for discrete event simulation
Proceedings of the 29th conference on Winter simulation
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
An Approximate Nonmyopic Computation for Value of Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Convex Optimization
Value of Information in Portfolio Decision Analysis
Decision Analysis
Approximation algorithms for budgeted learning problems
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Optimal Sequential Exploration: A Binary Learning Model
Decision Analysis
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
The ratio index for budgeted learning, with applications
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
The Value of Information and Intensity of Preference
Decision Analysis
Economic Analysis of Simulation Selection Problems
Management Science
VOILA: efficient feature-value acquisition for classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Bayesian Simulation and Decision Analysis: An Expository Survey
Decision Analysis
Sequential Sampling to Myopically Maximize the Expected Value of Information
INFORMS Journal on Computing
The conjunction of the knowledge gradient and the economic approach to simulation selection
Winter Simulation Conference
Budgeted learning of nailve-bayes classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Learning and classifying under hard budgets
ECML'05 Proceedings of the 16th European conference on Machine Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Decision Analysis
Goal-oriented sensor selection for intelligent phones: (GOSSIP)
Proceedings of the 2011 international workshop on Situation activity & goal awareness
Sequential Sampling with Economics of Selection Procedures
Management Science
Optimization via simulation with Bayesian statistics and dynamic programming
Proceedings of the Winter Simulation Conference
Value of information methods for pairwise sampling with correlations
Proceedings of the Winter Simulation Conference
Decreasing Marginal Value of Information Under Symmetric Loss
Decision Analysis
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We consider the Bayesian ranking and selection problem, in which one wishes to allocate an information collection budget as efficiently as possible to choose the best among several alternatives. In this problem, the marginal value of information is not concave, leading to algorithmic difficulties and apparent paradoxes. Among these paradoxes is that when there are many identical alternatives, it is often better to ignore some completely and focus on a smaller number than it is to spread the measurement budget equally across all the alternatives. We analyze the consequences of this nonconcavity in several classes of ranking and selection problems, showing that the value of information is “eventually concave,” i.e., concave when the number of measurements of each alternative is large enough. We also present a new fully sequential measurement strategy that addresses the challenge that nonconcavity it presents.