Paper #1160

Title:

Decision making in uncertain and changing environments

Authors:

Karl Schlag and Andriy Zapechelnyuk

Date:

June 2009

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.

Keywords:

Adaptive learning, experts, distribution-free, e-optimality, Hannan regret

JEL codes:

C44, D81, D83

Area of Research:

Microeconomics / Statistics, Econometrics and Quantitative Methods

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