Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint

Apostolos Burnetas; Odysseas Kanavetas

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arXiv · 1201.4002

Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint

Apostolos Burnetas·Odysseas Kanavetas

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Abstract

We consider the problem of sequential sampling from a finite number of independent statistical populations to maximize the expected infinite horizon average outcome per period, under a constraint that the expected average sampling cost does not exceed an upper bound. The outcome distributions are not known. We construct a class of consistent adaptive policies, under which the average outcome converges with probability 1 to the true value under complete information for all distributions with finite means. We also compare the rate of convergence for various policies in this class using simulation.

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BibTeX RIS

Apostolos Burnetas, Odysseas Kanavetas. 2012-01-19. Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint. https://arxiv.org/abs/1201.4002

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