Dynamic benchmark targeting
Karl Schlag () and
Journal of Economic Theory, 2017, vol. 169, issue C, 145-169
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; Regret minimization; Forecast combination; Non-Bayesian decision making (search for similar items in EconPapers)
JEL-codes: C44 D81 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
Working Paper: Dynamic Benchmark Targeting (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:jetheo:v:169:y:2017:i:c:p:145-169
Access Statistics for this article
Journal of Economic Theory is currently edited by A. Lizzeri and K. Shell
More articles in Journal of Economic Theory from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().