Learning in hidden Markov models with bounded memory
Daniel Monte () and
Maher Said ()
MPRA Paper from University Library of Munich, Germany
This paper explores the role of memory in decision making in dynamic environments. We examine the inference problem faced by an agent with bounded memory who receives a sequence of signals from a hidden Markov model. We show that the optimal symmetric memory rule may be deterministic. This result contrasts sharply with Hellman and Cover (1970) and Wilson (2004) and solves, for the context of a hidden Markov model, an open question posed by Kalai and Solan (2003).
Keywords: Bounded Memory; Hidden Markov Model; Randomization. (search for similar items in EconPapers)
JEL-codes: D82 D83 C72 C73 (search for similar items in EconPapers)
Date: 2010-06-23, Revised 2010-06-23
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https://mpra.ub.uni-muenchen.de/23854/1/MPRA_paper_23854.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/47595/8/MPRA_paper_47595.pdf revised version (application/pdf)
Journal Article: The value of (bounded) memory in a changing world (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:23854
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