Decision Making in Uncertain and Changing Environments
Karl Schlag () and
No 19, Discussion Papers from Kyiv School of Economics
We consider an agent who has to repeatedly make choices in an uncertain and changing environment, who has full information of the past, who discounts future payoffs, but who has no prior. We provide a learning algorithm that performs almost as well as the best of a given finite number of experts or benchmark strategies and does so at any point in time, provided the agent is sufficiently patient. The key is to find the appropriate degree of forgetting distant past. Standard learning algorithms that treat recent and distant past equally do not have the sequential epsilon optimality property.
Keywords: Adaptive learning; experts; distribution-free; epsilon-optimality; Hannan regret (search for similar items in EconPapers)
JEL-codes: C44 D81 D83 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-gth
Note: Under review in Review of Economic Studies
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http://repec.kse.org.ua/pdf/KSE_dp19.pdf First version, June 2009 (application/pdf)
Working Paper: Decision Making in Uncertain and Changing Environments (2009)
Working Paper: Decision making in uncertain and changing environments (2009)
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