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Dynamic benchmark targeting

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Dynamic benchmark targeting. / Schlag, Karl H.; Zapechelnyuk, Andriy.

In: Journal of Economic Theory, Vol. 169, 01.05.2017, p. 145-169.

Research output: Contribution to journalArticle

Harvard

Schlag, KH & Zapechelnyuk, A 2017, 'Dynamic benchmark targeting' Journal of Economic Theory, vol. 169, pp. 145-169. https://doi.org/10.1016/j.jet.2017.02.004

APA

Schlag, K. H., & Zapechelnyuk, A. (2017). Dynamic benchmark targeting. Journal of Economic Theory, 169, 145-169. https://doi.org/10.1016/j.jet.2017.02.004

Vancouver

Schlag KH, Zapechelnyuk A. Dynamic benchmark targeting. Journal of Economic Theory. 2017 May 1;169:145-169. https://doi.org/10.1016/j.jet.2017.02.004

Author

Schlag, Karl H. ; Zapechelnyuk, Andriy. / Dynamic benchmark targeting. In: Journal of Economic Theory. 2017 ; Vol. 169. pp. 145-169.

Bibtex - Download

@article{b9d407a9d5e543b5b666792c4b7b62c0,
title = "Dynamic benchmark targeting",
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.",
keywords = "Dynamic consistency, Experts, Forecast combination, Non-Bayesian decision making, Regret minimization",
author = "Schlag, {Karl H.} and Andriy Zapechelnyuk",
year = "2017",
month = "5",
day = "1",
doi = "10.1016/j.jet.2017.02.004",
language = "English",
volume = "169",
pages = "145--169",
journal = "Journal of Economic Theory",
issn = "0022-0531",
publisher = "Academic Press Inc.",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Dynamic benchmark targeting

AU - Schlag, Karl H.

AU - Zapechelnyuk, Andriy

PY - 2017/5/1

Y1 - 2017/5/1

N2 - 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.

AB - 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.

KW - Dynamic consistency

KW - Experts

KW - Forecast combination

KW - Non-Bayesian decision making

KW - Regret minimization

U2 - 10.1016/j.jet.2017.02.004

DO - 10.1016/j.jet.2017.02.004

M3 - Article

VL - 169

SP - 145

EP - 169

JO - Journal of Economic Theory

T2 - Journal of Economic Theory

JF - Journal of Economic Theory

SN - 0022-0531

ER -

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ID: 249597706