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Worth of perfect information in bernoulli bandits

Published online by Cambridge University Press:  01 July 2016

Donald A. Berry  and
Robert P. Kertz
Donald A. Berry*
Affiliation:
University of Minnesota
Robert P. Kertz*
Affiliation:
Georgia Institute of Technology
*
∗ Postal address: School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA. Supported in part by NSF Grants DMS 85-05023 and DMS 88-03087.
∗∗ Postal address: School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332, USA.
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Abstract

For k-armed Bernoulli bandits with discounting, sharp comparisons are given between average optimal rewards for a gambler and for a ‘perfectly informed' gambler, over natural collections of prior distributions. Some of these comparisons are proved under general discounting, and others under non-increasing discount sequences. Connections are made between these comparisons and the concept of ‘regret' in the minimax approach to bandit processes. Identification of extremal cases in the sharp comparisons is emphasized.

Keywords

BAYESIAN DECISION MAKINGMEASURE OF PERFECT INFORMATIONREGRETMINIMAX ANALYSISFAMILIES OF PRIOR DISTRIBUTIONS

Information

Type
Research Article
Information
Advances in Applied Probability , Volume 23 , Issue 1 , March 1991 , pp. 1 - 23
Copyright
Copyright © Applied Probability Trust 1991 

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Footnotes

Supported in part by NSF Grants DMS 86-01153 and DMS 88-01818.

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