Ranking and Contextual Selection

Context-Sensitive Simulation-Based Decisions When There Is No Time to Simulate Stochastic simulation is a powerful tool for discovering system design decisions that are the best possible (optimal) when averaged over real-world uncertainty. However, in applications such as personalized medicine and web content optimization, even better decisions can be made if they are tailored to specific, contemporaneous covariate information, such as patient health history and user reading habits. Unfortunately, in these and similar applications, there is no time to perform a refined simulation optimization. In “Ranking and Contextual Selection,” Keslin, Nelson, Pagnoncelli, Plumlee, and Rahimian use off-the-shelf simulation optimization methods to create a database of covariates and associated decisions that form a covariate-to-decision classifier and an upper confidence bound on its optimality gap when applied to covariates not in the database. A realistic example of web page assortment optimization is presented using a data set from Yahoo!.