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Recency, Consistent Learning, and Nash Equilibrium

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  • Fudenberg, Drew
  • Levine, David K.

Abstract

We examine the long-term implication of two models of learning with recency bias: recursive weights and limited memory. We show that both models generate similar beliefs and that both have a weighted universal consistency property. Using the limited-memory model we produce learning procedures that both are weighted universally consistent and converge with probability one to strict Nash equilibrium.

Suggested Citation

  • Fudenberg, Drew & Levine, David K., 2014. "Recency, Consistent Learning, and Nash Equilibrium," Scholarly Articles 13477947, Harvard University Department of Economics.
    • Handle: RePEc:hrv:faseco:13477947
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    File URL: http://dash.harvard.edu/bitstream/handle/1/13477947/165196/learning_with_recency_bias.pdf
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    Cited by:

    1. Ashley C. Craig & Ellen Garbarino & Stephanie A. Heger & Robert Slonim, 2017. "Waiting To Give: Stated and Revealed Preferences," Management Science, INFORMS, vol. 63(11), pages 3672-3690, November.
    2. Block, Juan I. & Fudenberg, Drew & Levine, David K., 2019. "Learning dynamics with social comparisons and limited memory," Theoretical Economics, Econometric Society, vol. 14(1), January.
    3. Gandré, Pauline, 2015. "Asset prices and information disclosure under recency-biased learning," CEPREMAP Working Papers (Docweb) 1515, CEPREMAP.
    4. Philipp Denter & John Morgan & Dana Sisak, 2022. "Showing Off or Laying Low? The Economics of Psych-outs," American Economic Journal: Microeconomics, American Economic Association, vol. 14(1), pages 529-580, February.
    5. Emerson Melo, 2021. "Learning In Random Utility Models Via Online Decision Problems," CAEPR Working Papers 2022-003 Classification-D, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    6. Ellison, Glenn & Fudenberg, Drew & Imhof, Lorens A., 2016. "Fast convergence in evolutionary models: A Lyapunov approach," Journal of Economic Theory, Elsevier, vol. 161(C), pages 1-36.
    7. Giovanna M. Invernizzi, 2020. "Public Information: Relevance or Salience?," Games, MDPI, vol. 11(1), pages 1-28, January.
    8. Juan I Block & Drew Fudenberg & David K Levine, 2017. "Learning Dynamics Based on Social Comparisons," Levine's Working Paper Archive 786969000000001375, David K. Levine.
    9. Leslie, David S. & Perkins, Steven & Xu, Zibo, 2020. "Best-response dynamics in zero-sum stochastic games," Journal of Economic Theory, Elsevier, vol. 189(C).

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