Common Learning

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 agents. Does it follow that the agents will commonly learn its value, i.e., that the true value of the parameter will become (approximate) common-knowledge? We show that the answer is affirmative when each agent's signal space is finite and show by example that common learning can fail when observations come from a countably infinite signal space.

Keywords: Common learning, Common belief, Private signals, Private beliefs

JEL Classification: D82, D83

Suggested Citation: Suggested Citation

Cripps, Martin and Cripps, Martin and Ely, Jeffrey C. and Mailath, George J. and Samuelson, Larry, Common Learning (August 2006). Cowles Foundation Discussion Paper No. 1575, Available at SSRN: https://ssrn.com/abstract=926517

Martin Cripps

University College London - Department of Economics ( email )

Gower Street
London
London, WC1E 6BT
United Kingdom

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
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314-935-4580 (Phone)
314-935-6359 (Fax)

Jeffrey C. Ely

Northwestern University - Department of Economics ( email )

2003 Sheridan Road
Evanston, IL 60208
United States
847-491-8219 (Phone)

George J. Mailath (Contact Author)

University of Pennsylvania - Department of Economics ( email )

Ronald O. Perelman Center for Political Science
133 South 36th Street
Philadelphia, PA 19104-6297
United States
215-898-7908 (Phone)
215-573-2057 (Fax)

HOME PAGE: http://web.sas.upenn.edu/gmailath/