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Comparative Learning Dynamics

Author

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  • James Bergin
  • Dan Bernhardt

Abstract

We study economic environments in which agents make choices on the basis of relative performance criteria and call the associated class of dynamic adjustment rules "comparative dynamics". We distinguish two classes of learning behavior: learning from the population experience (imitative dynamics) and learning only from one's own experience (introspective dynamics). Paradoxically, for a broad class of models, comparing stochastically stable states across dynamics, agent payoffs are lower for imitative than introspective dynamics-mimicking best practice in the population is counterproductive. Copyright 2004 by the Economics Department Of The University Of Pennsylvania And Osaka University Institute Of Social And Economic Research Association.

Suggested Citation

  • James Bergin & Dan Bernhardt, 2004. "Comparative Learning Dynamics," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(2), pages 431-465, May.
    • Handle: RePEc:ier:iecrev:v:45:y:2004:i:2:p:431-465
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    Citations

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    Cited by:

    1. Birgitte Sloth & Hans Whitta-Jacobsen, 2011. "Economic Darwinism," Theory and Decision, Springer, vol. 70(3), pages 385-398, March.
    2. John Duggan & Yoji Sekiya, 2009. "Voting Equilibria in Multi‐candidate Elections," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 11(6), pages 875-889, December.
    3. Vallée, Thomas & YIldIzoglu, Murat, 2009. "Convergence in the finite Cournot oligopoly with social and individual learning," Journal of Economic Behavior & Organization, Elsevier, vol. 72(2), pages 670-690, November.
    4. Sandholm, William H. & Izquierdo, Segismundo S. & Izquierdo, Luis R., 2020. "Stability for best experienced payoff dynamics," Journal of Economic Theory, Elsevier, vol. 185(C).
    5. Thomas Riechmann, 2006. "Cournot or Walras? Long-Run Results in Oligopoly Games," Journal of Institutional and Theoretical Economics (JITE), Mohr Siebeck, Tübingen, vol. 162(4), pages 702-720, December.
    6. Thomas Vallée & Murat Yıldızoğlu, 2013. "Can They Beat the Cournot Equilibrium? Learning with Memory and Convergence to Equilibria in a Cournot Oligopoly," Computational Economics, Springer;Society for Computational Economics, vol. 41(4), pages 493-516, April.
    7. Volkan Hacioglu, 2015. "Bayesian Expectations and Strategic Complementarity: Implications for Macroeconomic Stability," Post-Print hal-01404402, HAL.
    8. Ratul Lahkar, 2017. "Large Population Aggregative Potential Games," Dynamic Games and Applications, Springer, vol. 7(3), pages 443-467, September.
    9. Junyi Xu, 2021. "Reinforcement Learning in a Cournot Oligopoly Model," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1001-1024, December.
    10. Carlos Alós-Ferrer & Nick Netzer, 2015. "Robust stochastic stability," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 58(1), pages 31-57, January.
    11. Bergin, James & Bernhardt, Dan, 2009. "Cooperation through imitation," Games and Economic Behavior, Elsevier, vol. 67(2), pages 376-388, November.
    12. Hehenkamp, Burkhard & Wambach, Achim, 2010. "Survival at the center--The stability of minimum differentiation," Journal of Economic Behavior & Organization, Elsevier, vol. 76(3), pages 853-858, December.
    13. Alós-Ferrer, Carlos & Buckenmaier, Johannes, 2017. "Cournot vs. Walras: A reappraisal through simulations," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 257-272.

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