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Learning in hidden Markov models with bounded memory

Daniel Monte () and Maher Said ()

MPRA Paper from University Library of Munich, Germany

Abstract: 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
New Economics Papers: this item is included in nep-ore
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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)

Related works:
Journal Article: The value of (bounded) memory in a changing world (2014) Downloads
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