Efficient Learning Equilibrium

NeurIPS 2002 pp. 1635-1642

/neurips/2002/brafman2002neurips-efficient/

Abstract

We introduce efficient learning equilibrium (ELE), a normative ap(cid:173) proach to learning in non cooperative settings. In ELE, the learn(cid:173) ing algorithms themselves are required to be in equilibrium. In addition, the learning algorithms arrive at a desired value after polynomial time, and deviations from a prescribed ELE become ir(cid:173) rational after polynomial time. We prove the existence of an ELE in the perfect monitoring setting, where the desired value is the expected payoff in a Nash equilibrium. We also show that an ELE does not always exist in the imperfect monitoring case. Yet, it exists in the special case of common-interest games. Finally, we extend our results to general stochastic games.

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Text

Brafman and Tennenholtz. "Efficient Learning Equilibrium." Neural Information Processing Systems, 2002.

Markdown

[Brafman and Tennenholtz. "Efficient Learning Equilibrium." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/brafman2002neurips-efficient/)

BibTeX

@inproceedings{brafman2002neurips-efficient,
  title     = {{Efficient Learning Equilibrium}},
  author    = {Brafman, Ronen I. and Tennenholtz, Moshe},
  booktitle = {Neural Information Processing Systems},
  year      = {2002},
  pages     = {1635-1642},
  url       = {https://mlanthology.org/neurips/2002/brafman2002neurips-efficient/}
}