Quasi-Perfect Stackelberg Equilibrium
Abstract
Equilibrium refinements are important in extensive-form (i.e., tree-form) games, where they amend weaknesses of the Nash equilibrium concept by requiring sequential rationality and other beneficial properties. One of the most attractive refinement concepts is quasi-perfect equilibrium. While quasiperfection has been studied in extensive-form games, it is poorly understood in Stackelberg settings—that is, settings where a leader can commit to a strategy—which are important for modeling, for example, security games. In this paper, we introduce the axiomatic definition of quasi-perfect Stackelberg equilibrium. We develop a broad class of game perturbation schemes that lead to them in the limit. Our class of perturbation schemes strictly generalizes prior perturbation schemes introduced for the computation of (non-Stackelberg) quasi-perfect equilibria. Based on our perturbation schemes, we develop a branch-and-bound algorithm for computing a quasi-perfect Stackelberg equilibrium. It leverages a perturbed variant of the linear program for computing a Stackelberg extensive-form correlated equilibrium. Experiments show that our algorithm can be used to find an approximate quasi-perfect Stackelberg equilibrium in games with thousands of nodes.
Cite
Text
Marchesi et al. "Quasi-Perfect Stackelberg Equilibrium." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33012117Markdown
[Marchesi et al. "Quasi-Perfect Stackelberg Equilibrium." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/marchesi2019aaai-quasi/) doi:10.1609/AAAI.V33I01.33012117BibTeX
@inproceedings{marchesi2019aaai-quasi,
title = {{Quasi-Perfect Stackelberg Equilibrium}},
author = {Marchesi, Alberto and Farina, Gabriele and Kroer, Christian and Gatti, Nicola and Sandholm, Tuomas},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {2117-2124},
doi = {10.1609/AAAI.V33I01.33012117},
url = {https://mlanthology.org/aaai/2019/marchesi2019aaai-quasi/}
}