A Contextual Choice within a Fixed Candidate Enumeration

Authors

  • Timothy Elliott Author
  • David Rogers Author
  • Harry King Author

Abstract

Covariate-indexed design selection and fixed-list opportunity cost require explicit context weights and information available before action. Evaluation reports local losses and unrepresented contexts alongside the aggregate. It also keeps candidate discovery outside the fixed-list guarantee. Selecting well within known designs cannot establish that an omitted alternative would not be preferable. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.

References

  1. J. Du, S. Gao, and C.-H. Chen, “Selecting the Optimal System Design under Covariates,” Proceedings of the 2019 IEEE International Conference on Automation Science and Engineering, pp. 547–552, 2019.
  2. S. Gao and L. Shi, “An Optimal Opportunity Cost Selection Procedure for a Fixed Number of Designs,” Proceedings of the 2014 Winter Simulation Conference, pp. 3952–3958, 2014.
  3. Bertsimas, Dimitris, and Melvyn Sim. “The Price of Robustness.” Operations Research, vol. 52, no. 1, 2004, pp. 35–53. https://doi.org/10.1287/opre.1030.0065.
  4. Campi, Marco C., and Simone Garatti. “The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs.” SIAM Journal on Optimization, vol. 19, no. 3, 2008, pp. 1211–1230. https://doi.org/10.1137/07069821X.
  5. Glasserman, Paul. Monte Carlo Methods in Financial Engineering. Springer, 2004. https://doi.org/10.1007/978-0-387-21617-1.
  6. Ankenman, Bruce, Barry L. Nelson, and Jeremy Staum. “Stochastic Kriging for Simulation Metamodeling.” Operations Research, vol. 58, no. 2, 2010, pp. 371–382. https://doi.org/10.1287/opre.1090.0754.
  7. Kim, Seong-Hee, and Barry L. Nelson. “A Fully Sequential Procedure for Indifference-Zone Selection in Simulation.” ACM Transactions on Modeling and Computer Simulation, vol. 11, no. 3, 2001, pp. 251–273. https://doi.org/10.1145/502109.502111.
  8. Chick, Stephen E., and Koichiro Inoue. “New Two-Stage and Sequential Procedures for Selecting the Best Simulated System.” Operations Research, vol. 49, no. 5, 2001, pp. 732–743. https://doi.org/10.1287/opre.49.5.732.10615.
  9. Frazier, Peter I., Warren B. Powell, and Savas Dayanik. “A Knowledge-Gradient Policy for Sequential Information Collection.” SIAM Journal on Control and Optimization, vol. 47, no. 5, 2008, pp. 2410–2439. https://doi.org/10.1137/070693424.
  10. Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006. https://gaussianprocess.org/gpml/.

Downloads

Published

2026-06-13

Issue

Section

Articles