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Supplementary Appendix for ‘Non-Bayesian Updating: A Theoretical Framework’

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Author Info

  • Larry G. Epstein

    ()
    (Department of Economics, Boston University)

  • Jawwad Noor

    ()
    (Department of Economics, Boston University)

  • Alvaro Sandroni

    ()
    (Department of Economics, University of Pennsylvania)

Abstract

This appendix applies the model in ”Non-Bayesian Updating: A Theoretical Frame-Work” to address the question: What do non-Bayesian updaters learn?

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Bibliographic Info

Paper provided by Penn Institute for Economic Research, Department of Economics, University of Pennsylvania in its series PIER Working Paper Archive with number 08-017.

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Length: 18 pages
Date of creation: 18 Jan 2008
Date of revision:
Handle: RePEc:pen:papers:08-017

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Keywords: Non-Bayesian Learning;

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  1. Lawrence Blume & David Easley, 2001. "If You're So Smart, Why Aren't You Rich? Belief Selection in Complete and Incomplete Markets," Working Papers, Santa Fe Institute 01-06-031, Santa Fe Institute.
  2. E. Kalai & E. Lehrer, 2010. "Rational Learning Leads to Nash Equilibrium," Levine's Working Paper Archive 529, David K. Levine.
  3. Alvaro Sandroni, 2000. "Do Markets Favor Agents Able to Make Accurate Predicitions?," Econometrica, Econometric Society, Econometric Society, vol. 68(6), pages 1303-1342, November.
  4. Alvaro Sandroni, 2005. "Efficient markets and Bayes’ rule," Economic Theory, Springer, Springer, vol. 26(4), pages 741-764, November.
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