Common Learning

Martin William Cripps, Jeffrey Ely, George J. Mailath () and Larry Samuelson
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Larry Samuelson: University of Wisconsin

No 1575R, Cowles Foundation Discussion Papers from Cowles Foundation, Yale University

Abstract: Consider two agents who learn the value of an unknown parameter by observing a sequence of private signals. The signals are independent and identically distributed across time but not necessarily across agents. We show that that when each agent's signal space is finite, the agents will commonly learn its value, i.e., that the true value of the parameter will become approximate common-knowledge. In contrast, if the agents' observations come from a countably infinite signal space, then this contraction mapping property fails. We show by example that common learning can fail in this case.

Keywords: Common learning; Common belief; Private signals; Private beliefs (search for similar items in EconPapers)
JEL-codes: D82 D83 (search for similar items in EconPapers)
Date: 2006-08, Revised 2007-06
Note: CFP 1257.
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Published in Econometrics (2008), 76(4): 909-933

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http://cowles.econ.yale.edu/P/cd/d15b/d1575-r.pdf (application/pdf)

Related works:
Working Paper: Common Learning (2006)
Working Paper: Common Learning (2006)
Working Paper: Common Learning (2007)
Journal Article: Common Learning (2008)
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