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Learning in hidden Markov models with bounded memory

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  • Monte, Daniel
  • Said, Maher

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).

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File URL: http://mpra.ub.uni-muenchen.de/23854/
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File URL: http://mpra.ub.uni-muenchen.de/47595/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 23854.

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Date of creation: 23 Jun 2010
Date of revision: 23 Jun 2010
Handle: RePEc:pra:mprapa:23854

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Keywords: Bounded Memory; Hidden Markov Model; Randomization.;

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  1. Barton L. Lipman, 1995. "Information Processing and Bounded Rationality: A Survey," Canadian Journal of Economics, Canadian Economics Association, Canadian Economics Association, vol. 28(1), pages 42-67, February.
  2. Harold L. Cole & Narayana R. Kocherlakota, 2001. "Finite memory and imperfect monitoring," Staff Report, Federal Reserve Bank of Minneapolis 287, Federal Reserve Bank of Minneapolis.
  3. Marco Battaglini, 2003. "Long-Term Contracting with Markovian Consumers," Theory workshop papers, UCLA Department of Economics 505798000000000048, UCLA Department of Economics.
  4. Andrew Postlewaite & Olivier Compte, 2009. "Plausible Cooperation, Second Version," PIER Working Paper Archive 10-039, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 16 Dec 2010.
  5. Kalai, Ehud & Solan, Eilon, 2003. "Randomization and simplification in dynamic decision-making," Journal of Economic Theory, Elsevier, Elsevier, vol. 111(2), pages 251-264, August.
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