On the robustness of equilibria in generalized aggregative games

F. Fabiani, K. Margellos and P. J. Goulart

in 2020 IEEE Conference on Decision and Control (CDC), Jeju Island, Korea, December 2020.
BibTeX 

@inproceedings{FMG:2020,
  author = {F. Fabiani and K. Margellos and P. J. Goulart},
  title = {On the robustness of equilibria in generalized aggregative games},
  booktitle = {2020 IEEE Conference on Decision and Control (CDC)},
  year = {2020}
}

We address the problem of assessing the robustness of the equilibria in uncertain, multi-agent games. Specifically, we focus on generalized Nash equilibrium problems in aggregative form subject to linear coupling constraints affected by uncertainty with a possibly unknown probability distribution. Within a data-driven context, we apply the scenario approach paradigm to provide a-posteriori feasibility certificates for the entire set of generalized Nash equilibria of the game. Then, we show that assessing the violation probability of such set merely requires to enumerate the constraints that “shape” it. For the class of aggregative games, this results in solving a feasibility problem on each active facet of the feasibility region, for which we propose a semi-decentralized algorithm. We demonstrate our theoretical results by means of an academic example.

Probabilistic feasibility guarantees for solution sets to uncertain variational inequalities

F. Fabiani, K. Margellos and P. J. Goulart

May 2020.
BibTeX  Preprint 

@article{FMG:2020b,
  author = {F. Fabiani and K. Margellos and P. J. Goulart},
  title = {Probabilistic feasibility guarantees for solution sets to uncertain variational inequalities},
  year = {2020}
}

We leverage on a data-driven paradigm to provide a-posteriori feasibility certificates to the set of solutions to variational inequalities affected by uncertainty. Specifically, we focus on instances with a deterministic mapping and an uncertain feasibility set, and represent uncertainty by means of scenarios. Building upon recent advances in the scenario approach literature, we quantify the robustness properties of the entire set of solutions against a new unseen realization of the uncertainty. This allows us to circumvent the necessity that the variational inequality admits a unique solution. We show that assessing the violation probability of the entire set of solutions rather than a single one requires enumeration of the support constraints that ‘‘shape’’ this set. In this context, we also propose a general procedure to enumerate the support constraints that does not need a closed form description of the solution set, which is unlikely to be available. We show that robust game theory constitutes an applications class that falls within the considered framework of uncertain variational inequalities, and illustrate our theoretical results through extensive numerical simulations on a case study involving an electric vehicle charging coordination problem.