EconPapers    
Economics at your fingertips  
 

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: C72 C73 D82 D83 (search for similar items in EconPapers)
Date: 2010-06-23, Revised 2010-06-23
New Economics Papers: this item is included in nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
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
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:23854

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().

 
Page updated 2025-03-30
Handle: RePEc:pra:mprapa:23854