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Rational Expectations and Rational Learning


  • Lawrence Blume

    (Cornell University)

  • David Easley

    (Cornell University)


We provide an overview of the methods of analysis and results obtained, and, most important, an assessment of the success of rational learning dynamics in tying down limit beliefs and limit behavior. We illustrate the features common to rational or Bayesian learning in single agent, game theoretic and equilibrium frameworks. We show that rational learing is possible in each of these environments. The issue is not in whether rational learning can occur, but in what results it produces. If we assume a natural complex parameterization of the choice environment all we know is the rational learner believes that his posteriors will converge somewhere with prior probability one. Alternatively, if we, the modelers, assume the simple parameterization of the choice environment that is necessary to obtain positive results we are closing our models in the ad hoc fashion that rational learning was inroduced to avoid. We believe that a partial resolution of this conundrum is to pay more attention to how learning interacts with other dynamic forces. We show that in a simple economy, the forces of market selection can yield convergence to rational expectations equilibria even without every agent behaving as a rational learner.

Suggested Citation

  • Lawrence Blume & David Easley, 1993. "Rational Expectations and Rational Learning," Game Theory and Information 9307003, EconWPA.
  • Handle: RePEc:wpa:wuwpga:9307003
    Note: 43 pages, plain TeX, no figures.

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    References listed on IDEAS

    1. Bergstrom, Theodore C, 1995. "On the Evolution of Altruistic Ethical Rules for Siblings," American Economic Review, American Economic Association, vol. 85(1), pages 58-81, March.
    2. Ellison, Glenn, 1993. "Learning, Local Interaction, and Coordination," Econometrica, Econometric Society, vol. 61(5), pages 1047-1071, September.
    3. Kandori, Michihiro & Mailath, George J & Rob, Rafael, 1993. "Learning, Mutation, and Long Run Equilibria in Games," Econometrica, Econometric Society, vol. 61(1), pages 29-56, January.
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    Cited by:

    1. A. Arrighetti & S. Curatolo, 2010. "Opportunismo e coordinamento: soluzioni regolative e istituzionali," Economics Department Working Papers 2010-EP02, Department of Economics, Parma University (Italy).
    2. Marschall, Paul, 2001. "Lernen und Lebensstilwandel in Transformationsökonomien," Wirtschaftswissenschaftliche Diskussionspapiere 07/2001, University of Greifswald, Faculty of Law and Economics.
    3. Holden, Tom, 2008. "Rational macroeconomic learning in linear expectational models," MPRA Paper 10872, University Library of Munich, Germany.
    4. Ennis, Huberto M. & Keister, Todd, 2005. "Government policy and the probability of coordination failures," European Economic Review, Elsevier, vol. 49(4), pages 939-973, May.
    5. John H. Nachbar, 1997. "Prediction, Optimization, and Learning in Repeated Games," Econometrica, Econometric Society, vol. 65(2), pages 275-310, March.
    6. Huberto M. Ennis & Todd Keister, 2001. "Optimal policy with probabilistic equilibrium selection," Working Paper 01-03, Federal Reserve Bank of Richmond.
    7. Ehud Kalai & Ehud Lehrer, 1990. "Merging Economic Forecasts," Discussion Papers 1035, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    8. José Penalva & Michael D. Ryall, 2001. "Causal assessment in finite extensive-form games," Economics Working Papers 483, Department of Economics and Business, Universitat Pompeu Fabra, revised Sep 2003.
    9. Huberto Ennis & Todd Keister, 2000. "Government Policy and Probabilistic Equilibrium Selection," Econometric Society World Congress 2000 Contributed Papers 1148, Econometric Society.
    10. Kalai, Ehud & Lehrer, Ehud, 1993. "Rational Learning Leads to Nash Equilibrium," Econometrica, Econometric Society, vol. 61(5), pages 1019-1045, September.
    11. Jose Penalva-Zuasti & Michael D. Ryall, 2003. "Causal Assessment in Finite-length Extensive-Form Games," Levine's Working Paper Archive 506439000000000074, David K. Levine.
    12. Simon D Woodcock, 2002. "Modeling Labor Markets with Heterogeneous Agents and Matches," Longitudinal Employer-Household Dynamics Technical Papers 2002-19, Center for Economic Studies, U.S. Census Bureau.
    13. Lagunoff, Roger, 1997. "On the dynamic selection of mechanisms for provision of public projects," Journal of Economic Dynamics and Control, Elsevier, vol. 21(10), pages 1699-1725, August.
    14. Simon D Woodcock, 2002. "Agent Heterogeneity and Learning: An Application to Labor Markets," Longitudinal Employer-Household Dynamics Technical Papers 2002-20, Center for Economic Studies, U.S. Census Bureau.
    15. Sögner, Leopold, 2015. "Learning, convergence and economic constraints," Mathematical Social Sciences, Elsevier, vol. 75(C), pages 27-43.

    More about this item

    JEL classification:

    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty


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