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Learning in hidden Markov models with bounded memory

  • Monte, Daniel
  • Said, Maher

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/23854/1/MPRA_paper_23854.pdf
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File URL: http://mpra.ub.uni-muenchen.de/47595/8/MPRA_paper_47595.pdf
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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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  1. Marco Battaglini, 2005. "Long-Term Contracting with Markovian Consumers," American Economic Review, American Economic Association, vol. 95(3), pages 637-658, June.
  2. Olivier Compte & Andrew Postlewaite, 2007. "Effecting Cooperation," PIER Working Paper Archive 09-019, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 29 May 2009.
  3. Harold L. Cole & Narayana R. Kocherlakota, 2001. "Finite memory and imperfect monitoring," Staff Report 287, Federal Reserve Bank of Minneapolis.
  4. Barton L. Lipman, 1993. "Information Processing and Bounded Rationality: A Survey," Working Papers 872, Queen's University, Department of Economics.
  5. Kalai, Ehud & Solan, Eilon, 2003. "Randomization and simplification in dynamic decision-making," Journal of Economic Theory, Elsevier, vol. 111(2), pages 251-264, August.
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