Document Type
Discussion Paper
Publication Date
8-1-2006
CFDP Number
1575R
CFDP Revision Date
2007-06-01
CFDP Pages
33
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.
Recommended Citation
Cripps, Martin W.; Ely, Jeffrey C.; Mailath, George J.; and Samuelson, Larry, "Common Learning" (2006). Cowles Foundation Discussion Papers. 1867.
https://elischolar.library.yale.edu/cowles-discussion-paper-series/1867