Dynamic benchmark targeting

Karl H. Schlag, Andriy Zapechelnyuk*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)145-169
    Number of pages25
    JournalJournal of Economic Theory
    Early online date21 Feb 2017
    Publication statusPublished - 1 May 2017


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


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