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

In: Applications of Mathematics and Informatics in Military Science

Author

Listed:
  • Apostolos Burnetas

    (University of Athens)

  • Odysseas Kanavetas

    (University of Athens)

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.

Suggested Citation

  • Apostolos Burnetas & Odysseas Kanavetas, 2012. "Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint," Springer Optimization and Its Applications, in: Nicholas J. Daras (ed.), Applications of Mathematics and Informatics in Military Science, edition 127, chapter 0, pages 97-112, Springer.
    • Handle: RePEc:spr:spochp:978-1-4614-4109-0_8
    DOI: 10.1007/978-1-4614-4109-0_8
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