Algorithmic Game Theory

Algorithmic Game Theory is a branch of theoretical computer science that studies the design and analysis of algorithms in strategic environments where rational agents make decisions based on self-interest, integrating concepts from game theory and algorithmic complexity.

4.2K

Publications

218.3K

Citations

6.4K

Authors

1.8K

Institutions

Publications per year

2017–2026

986

Authors

6.4K

  1. TS
    Tüomas Sandholm

    Carnegie Mellon University, United States

    65 publications · 4K citations

    31

    h-index

  2. MT
    Milind Tambe

    University of Southern California, United States

    47 publications · 3.3K citations

    29

    h-index

  3. VC
    Vincent Conitzer

    Duke University, United States

    34 publications · 2.9K citations

    26

    h-index

  4. MB
    Michael Bowling

    University of Alberta, Canada

    28 publications · 2.8K citations

    24

    h-index

  5. MW
    Michael Wooldridge

    University of Liverpool, United Kingdom

    29 publications · 1.1K citations

    21

    h-index

1–5 of 6.4K

Venues

  1. Games and Economic Behavior

    103 publications · 7K citations

    44

    h-index

  2. International Journal of Game Theory

    89 publications · 9.6K citations

    34

    h-index

  3. Journal of Economic Theory

    JET · 62 publications · 5.4K citations

    32

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 93 publications · 3K citations

    31

    h-index

  5. arXiv (Cornell University)

    Repository · 151 publications · 4.4K citations

    29

    h-index

1–5