A Threshold Search before Robust Tradeoffs Are Settled

Authors

  • Zachary Rogers Author
  • Steven Hoffman Author
  • Henry Perry Author

Abstract

Peak-directed exploration can discover alternatives before robust multi-objective preferences are fixed. Evaluation should vary search coverage separately from tradeoff weights and preserve component outputs. The search threshold is an exploration device, not a substantive adequacy standard. Additional samples can resolve estimation uncertainty but cannot choose the decision maker's preferred tradeoff. 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. Gao, L. Shi, and Z. Zhang, “A Peak-Over-Threshold Search Method for Global Optimization,” Automatica, vol. 89, pp. 83–91, 2018.

W. Liu, S. Gao, and L. H. Lee, “A Multi-Objective Perspective on Robust Ranking and Selection,” Proceedings of the 2017 Winter Simulation Conference, pp. 2116–2127, 2017.

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/.

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.

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Published

2026-09-28

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Articles