A Converging Sequence of Myopic Measurements

Authors

  • Christopher Butler Author
  • Tyler Young Author
  • Oliver Barrett Author

Abstract

Myopic measurement values and convergence of allocation describe how local decisions accumulate. Practical evaluation should retain initial evidence and complete paths, rather than reading a stable estimate as successful learning. Independent terminal checks over finite budgets can show whether the sequence revisits competitive designs and resolves the comparisons that matter before commitment. 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. Y. Li, S. Gao, and T. Shi, “Asymptotic Optimality of Myopic Ranking and Selection Procedures,” Automatica, vol. 151, 110896, 2023.
  2. Y. Li and S. Gao, “On the Convergence of Optimal Computing Budget Allocation Algorithms,” Proceedings of the 2021 Winter Simulation Conference, pp. 1–12, 2021.
  3. Li, Y., Wang, H., Shang, X., Tang, X., Cao, Y., & Chen, X. (2026). BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models. arXiv preprint arXiv:2605.09134.
  4. Glasserman, Paul. Monte Carlo Methods in Financial Engineering. Springer, 2004. https://doi.org/10.1007/978-0-387-21617-1.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006. https://gaussianprocess.org/gpml/.
  10. Sacks, Jerome, William J. Welch, Toby J. Mitchell, and Henry P. Wynn. “Design and Analysis of Computer Experiments.” Statistical Science, vol. 4, no. 4, 1989, pp. 409–423. https://doi.org/10.1214/ss/1177012413.
  11. 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.

Downloads

Published

2025-11-19

Versions

Issue

Vol. 1 No. 11 (2025): November 2025

Section

Articles