Monte, Daniel and Said, Maher (2010): Learning in hidden Markov models with bounded memory.
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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).
|Item Type:||MPRA Paper|
|Original Title:||Learning in hidden Markov models with bounded memory|
|Keywords:||Bounded Memory; Hidden Markov Model; Randomization.|
|Subjects:||D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D82 - Asymmetric and Private Information ; Mechanism Design
D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D83 - Search ; Learning ; Information and Knowledge ; Communication ; Belief ; Unawareness
C - Mathematical and Quantitative Methods > C7 - Game Theory and Bargaining Theory > C72 - Noncooperative Games
C - Mathematical and Quantitative Methods > C7 - Game Theory and Bargaining Theory > C73 - Stochastic and Dynamic Games ; Evolutionary Games ; Repeated Games
|Depositing User:||Maher Said|
|Date Deposited:||13. Jul 2010 12:31|
|Last Modified:||23. Feb 2013 21:48|
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