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Common Learning

Martin Cripps, Jeffrey Ely, George Mailath () and Larry Samuelson ()
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Larry Samuelson: Social Science Computing Cooperative, University of Wisconsin Madison

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

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)
Pages: 33 pages
Date: 2007-06-20
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Related works:
Journal Article: Common Learning (2008) Downloads
Working Paper: Common Learning (2007) Downloads
Working Paper: Common Learning (2006) Downloads
Working Paper: Common Learning (2006) Downloads
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