Bandit bounds from stochastic variability extrema

Stephen J. Herschkorn

Statistics & Probability Letters, 1997, vol. 35, issue 3, 283-288

Abstract: In the consideration of bandit problems with general rewards and discount sequences, we compare an arm to one whose reward distribution may be one of two degenerate distributions. For the general multi-armed case, the latter problem provides an upper bound on the optimal return. In the case of two arms with the second known and regular discounting, consideration of the two-point distribution provides a sufficient condition for stopping. We interpret these results in the context of the value of information. The results, and others in the literature, suggest that bandit thresholds (or indices) may be monotonic with respect to ordering of distributions in the convex sense.

Keywords: Bandit; problems; stochastic; variability; ordering; convex; ordering; value; of; information (search for similar items in EconPapers)
Date: 1997
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