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Dynamic Benchmark Targeting

Author

Listed:
  • Karl H. Schlag
  • Andriy Zapechelnyuk
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

  • Karl H. Schlag & Andriy Zapechelnyuk, 2016. "Dynamic Benchmark Targeting," Working Papers 2016_20, Business School - Economics, University of Glasgow.
    • Handle: RePEc:gla:glaewp:2016_20
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Schlag, Karl, 2018. "How to Play Out of Equilibrium: Beating the Average," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181525, Verein für Socialpolitik / German Economic Association.
    2. Korotkov, Vladimir & Wu, Desheng, 2021. "Benchmarking project portfolios using optimality thresholds," Omega, Elsevier, vol. 99(C).
    3. Karl Schlag & Andriy Zapechelnyuk, 2017. "Robust Sequential Search," Discussion Paper Series, School of Economics and Finance 201803, School of Economics and Finance, University of St Andrews, revised 05 Mar 2020.
    4. René Caldentey & Ying Liu & Ilan Lobel, 2017. "Intertemporal Pricing Under Minimax Regret," Operations Research, INFORMS, vol. 65(1), pages 104-129, February.

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    More about this item

    Keywords

    Dynamic consistency; experts; regret minimization; forecast com- bination; non-Bayesian decision making.;
    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

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