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Deterministic Calibration and Nash Equilibrium

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Learning Theory (COLT 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3120))

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Abstract

We provide a natural learning process in which the joint frequency of empirical play converges into the set of convex combinations of Nash equilibria. In this process, all players rationally choose their actions using a public prediction made by a deterministic, weakly calibrated algorithm. Furthermore, the public predictions used in any given round of play are frequently close to some Nash equilibrium of the game.

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Author information

Authors and Affiliations

  1. University of Pennsylvania, Philadelphia, PA, 19104, USA

    Sham M. Kakade & Dean P. Foster

Authors
  1. Sham M. Kakade
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  2. Dean P. Foster
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Editor information

Editors and Affiliations

  1. The Centre for Computational Statistics and Machine Learning Department of Computer Science, University College London, Gower St., WC1E 6BT, London

    John Shawe-Taylor

  2. Google, 1600 Amphitheater Parkway, CA 94043, Mountain View, USA

    Yoram Singer

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© 2004 Springer-Verlag Berlin Heidelberg

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Kakade, S.M., Foster, D.P. (2004). Deterministic Calibration and Nash Equilibrium. In: Shawe-Taylor, J., Singer, Y. (eds) Learning Theory. COLT 2004. Lecture Notes in Computer Science(), vol 3120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27819-1_3

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  • DOI: https://doi.org/10.1007/978-3-540-27819-1_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22282-8

  • Online ISBN: 978-3-540-27819-1

  • eBook Packages: Springer Book Archive

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