Common learning with intertemporal dependence

Martin W. Cripps, Jeffrey C. Ely, George J. Mailath, Larry Samuelson

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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 et al., Econometrica, 76(4):909-933, 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.

Original languageEnglish (US)
Pages (from-to)55-98
Number of pages44
JournalInternational Journal of Game Theory
Volume42
Issue number1
DOIs
StatePublished - Jan 1 2013

Keywords

  • Common belief
  • Common learning
  • Private beliefs
  • Private signals

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics (miscellaneous)
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

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