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Decision making in uncertain and changing environments

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  • Karl Schlag
  • Andriy Zapechelnyuk

Abstract

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.

Suggested Citation

  • Karl Schlag & Andriy Zapechelnyuk, 2009. "Decision making in uncertain and changing environments," Economics Working Papers 1160, Department of Economics and Business, Universitat Pompeu Fabra.
    • Handle: RePEc:upf:upfgen:1160
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    References listed on IDEAS

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    Cited by:

    1. William A. Brock & Steven N. Durlauf, 2015. "On Sturdy Policy Evaluation," The Journal of Legal Studies, University of Chicago Press, vol. 44(S2), pages 447-473.

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    More about this item

    Keywords

    Adaptive learning; experts; distribution-free; e-optimality; Hannan regret;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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