Management Science
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Principles of Corporate Finance with Cdrom
Principles of Corporate Finance with Cdrom
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Comparisons with a Standard in Simulation Experiments
Management Science
A Multiple Attribute Utility Theory Approach to Ranking and Selection
Management Science
Stochastic Optimal Control: The Discrete-Time Case
Stochastic Optimal Control: The Discrete-Time Case
Selecting a Selection Procedure
Management Science
Bayesian Simulation and Decision Analysis: An Expository Survey
Decision Analysis
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
The conjunction of the knowledge gradient and the economic approach to simulation selection
Winter Simulation Conference
Sequential Sampling with Economics of Selection Procedures
Management Science
A Framework for Selecting a Selection Procedure
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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
Guessing preferences: a new approach to multi-attribute ranking and selection
Proceedings of the Winter Simulation Conference
Optimal learning for sequential sampling with non-parametric beliefs
Journal of Global Optimization
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Ranking and selection procedures are standard methods for selecting the best of a finite number of simulated design alternatives based on a desired level of statistical evidence for correct selection. But the link between statistical significance and financial significance is indirect, and there has been little or no research into it. This paper presents a new approach to the simulation selection problem, one that maximizes the expected net present value of decisions made when using stochastic simulation. We provide a framework for answering these managerial questions: When does a proposed system design, whose performance is unknown, merit the time and money needed to develop a simulation to infer its performance? For how long should the simulation analysis continue before a design is approved or rejected? We frame the simulation selection problem as a “stoppable” version of a Bayesian bandit problem that treats the ability to simulate as a real option prior to project implementation. For a single proposed system, we solve a free boundary problem for a heat equation that approximates the solution to a dynamic program that finds optimal simulation project stopping times and that answers the managerial questions. For multiple proposed systems, we extend previous Bayesian selection procedures to account for discounting and simulation-tool development costs.