Common Learning with Intertemporal Dependence
Consider two agents who learn the value of an unknown parameter by observing a sequence of private signals. Will the agents commonly learn the value of the parameter, i.e., will the true value of the parameter become approximate common-knowledge? If the signals are independent and identically distributed across time (but not necessarily across agents), the answer is yes (Cripps, Ely, Mailath, and Samuelson, 2008). This paper explores the implications of allowing the signals to be dependent over time. We present a counterexample showing that even extremely simple time dependence can preclude common learning, and present sufficient conditions for common learning.
|Date of creation:||11 May 2011|
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- Martin W. Cripps & Jeffrey C. Ely & George J. Mailath & Larry Samuelson, 2006.
Cowles Foundation Discussion Papers
1575R, Cowles Foundation for Research in Economics, Yale University, revised Jun 2007.
- Martin W. Cripps & Jeffrey C. Ely & George J. Mailath & Larry Samuelson, 2007. "Common Learning," PIER Working Paper Archive 07-018, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- Martin W. Cripps & Jeffrey C. Ely & George J. Mailath & Larry Samuelson, 2006. "Common Learning," Cowles Foundation Discussion Papers 1575, Cowles Foundation for Research in Economics, Yale University.
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1484, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
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