Tight Inapproximability for Graphical Games
Abstract
We provide a complete characterization for the computational complexity of finding approximate equilibria in two-action graphical games. We consider the two most well-studied approximation notions: ε-Nash equilibria (ε-NE) and ε-well-supported Nash equilibria (ε-WSNE), where ε is in [0,1]. We prove that computing an ε-NE is PPAD-complete for any constant ε smaller than 1/2, while a very simple algorithm (namely, letting all players mix uniformly between their two actions) yields a 1/2-NE. On the other hand, we show that computing an ε-WSNE is PPAD-complete for any constant ε smaller than 1, while a 1-WSNE is trivial to achieve, because any strategy profile is a 1-WSNE. All of our lower bounds immediately also apply to graphical games with more than two actions per player.
Cite
Text
Deligkas et al. "Tight Inapproximability for Graphical Games." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I5.25695Markdown
[Deligkas et al. "Tight Inapproximability for Graphical Games." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/deligkas2023aaai-tight/) doi:10.1609/AAAI.V37I5.25695BibTeX
@inproceedings{deligkas2023aaai-tight,
title = {{Tight Inapproximability for Graphical Games}},
author = {Deligkas, Argyrios and Fearnley, John and Hollender, Alexandros and Melissourgos, Themistoklis},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2023},
pages = {5600-5607},
doi = {10.1609/AAAI.V37I5.25695},
url = {https://mlanthology.org/aaai/2023/deligkas2023aaai-tight/}
}