@inproceedings{7e0bd3959c644238b9fb0c1041f38d10,
title = "Selecting the Best Simulated System: Thinking Differently About an Old Problem",
abstract = "The methods known collectively as “ranking & selection” have been a theoretical and practical success story for the optimization of simulated stochastic systems: they are widely used in practice, have been implemented in commercial simulation software, and research has made them more and more statistically efficient. However, “statistically efficient” has meant minimizing the number of simulation-generated observations required to make a selection, or maximizing the strength of the inference given a budget of observations. Exploiting high-performance computing, and specifically the capability to simulate many feasible solutions in parallel, has challenged the ranking & selection paradigm. In this paper we review the challenge and suggest an entirely different approach.",
keywords = "Parallel simulation, Ranking & selection, Simulation optimization, Stochastic simulation",
author = "Nelson, {Barry L.}",
year = "2020",
doi = "10.1007/978-3-030-43465-6_3",
language = "English (US)",
isbn = "9783030434649",
series = "Springer Proceedings in Mathematics and Statistics",
publisher = "Springer",
pages = "69--79",
editor = "Bruno Tuffin and Pierre L{\textquoteright}Ecuyer",
booktitle = "Monte Carlo and Quasi-Monte Carlo Methods, MCQMC 2018",
note = "13th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, MCQMC 2018 ; Conference date: 01-07-2018 Through 06-07-2018",
}