f01d1b6e_ns__Information__and_Decisions__Title
[Date] December 2, 2011 [Presenter] Alessandro Arlotto [Title] 
Markov Decision Problems where Means bound Variances We consider a rich class of finite-horizon Markov decision problems (MDPs) that have a natural dynamic self-bounding property, and we show that for such problems the variance of the optimal total reward can be bounded in terms of its expected value. This provides an easily used measure of the uncertainty of the realized reward, and it leads to a weak law of large numbers for the realized reward. The applicability of this bound and some simple criteria for an MDP to be self-bounding are illustrated by examples of self-bounding MDPs from classical problems in operations research, operations management, financial engineering, and combinatorial optimization. Joint work with Noah Gans and J. Michael Steele (Wharton).
Seminars / Conferences - Operations, Information, and Decisions
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