Approximate Nash Equilibrium

An Approximate Nash Equilibrium is a strategy profile in which no player can achieve a significantly improved payoff by unilaterally deviating, satisfying an ε-tolerance criterion relative to a true Nash Equilibrium within a non-cooperative game.

224

Publications

11.7K

Citations

385

Authors

206

Institutions

Publications per year

2017–2026

67

Variants

ε-Nash Equilibrium

Authors

385

  1. ML
    Marc Lanctot

    Google DeepMind (United Kingdom), United Kingdom

    13 publications · 732 citations

    12

    h-index

  2. PW
    Paul W. Goldberg

    University of Liverpool, United Kingdom

    7 publications · 199 citations

    7

    h-index

  3. JF
    John Fearnley

    University of Liverpool, United Kingdom

    7 publications · 174 citations

    7

    h-index

  4. AS
    Alexander Skopalik

    RWTH Aachen University, Germany

    7 publications · 381 citations

    7

    h-index

  5. CD
    Constantinos Daskalakis

    Massachusetts Institute of Technology, United States

    7 publications · 610 citations

    7

    h-index

1–5 of 385

Venues

  1. arXiv (Cornell University)

    Repository · 24 publications · 1.4K citations

    14

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 8 publications · 159 citations

    8

    h-index

  3. The Annals of Applied Probability

    7 publications · 493 citations

    7

    h-index

  4. Mathematics of Operations Research

    7 publications · 347 citations

    7

    h-index

  5. Algorithmica

    5 publications · 203 citations

    5

    h-index

1–5