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Dynamic benchmark targeting

Author

Listed:
  • Schlag, Karl H.
  • Zapechelnyuk, Andriy

Abstract

We study decision making in complex discrete-time dynamic environments where Bayesian optimization is intractable. A decision maker is equipped with a finite set of benchmark strategies. She aims to perform similarly to or better than each of these benchmarks. Furthermore, she cannot commit to any decision rule, hence she must satisfy this goal at all times and after every history. We find such a rule for a sufficiently patient decision maker and show that it necessitates not to rely too much on observations from distant past. In this sense we find that it can be optimal to forget.

Suggested Citation

  • Schlag, Karl H. & Zapechelnyuk, Andriy, 2017. "Dynamic benchmark targeting," Journal of Economic Theory, Elsevier, vol. 169(C), pages 145-169.
  • Handle: RePEc:eee:jetheo:v:169:y:2017:i:c:p:145-169
    DOI: 10.1016/j.jet.2017.02.004
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    More about this item

    Keywords

    Dynamic consistency; Experts; Regret minimization; Forecast combination; Non-Bayesian decision making;

    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

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