A Contextual Rule Prepared at an Offline Computing Rate

Authors

  • Lucas Phillips Author
  • Kyle Peterson Author
  • Jason Matthews Author

Abstract

Offline rate analysis and contextual allocation meet in the evidence available before future covariates arrive. Validation should hold out operating contexts and freeze the rule. It reports local decision errors beside population-weighted loss. Application outcomes acquired later can define an update policy, but must not leak into the original preparation claim. 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

S. Cakmak, Y. Wang, S. Gao, and E. Zhou, “Contextual Ranking and Selection with Gaussian Processes and Optimal Computing Budget Allocation,” ACM Transactions on Modeling and Computer Simulation, vol. 34, no. 2, pp. 1–24, 2024.

S. Gao, C. Li, and J. Du, “Rate Analysis for Offline Simulation Online Application,” Proceedings of the 2019 Winter Simulation Conference, pp. 3468–3479, 2019.

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.

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.

Glasserman, Paul. Monte Carlo Methods in Financial Engineering. Springer, 2004. https://doi.org/10.1007/978-0-387-21617-1.

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.

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.

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.

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.

Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006. https://gaussianprocess.org/gpml/.

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Published

2025-03-17

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Articles