Personalized incentives as feedback design in generalized Nash equilibrium problems

Filippo Fabiani, Andrea Simonetto and Paul J. Goulart

@article{FSG:2023,
  author = {Fabiani, Filippo and Simonetto, Andrea and Goulart, Paul J.},
  title = {Personalized incentives as feedback design in generalized Nash equilibrium problems},
  journal = {IEEE Transactions on Automatic Control},
  year = {2023},
  pages = {1-16},
  url = {https://ieeexplore.ieee.org/document/10155144},
  doi = {10.1109/TAC.2023.3287218}
}

We investigate both stationary and time-varying, nonmonotone generalized Nash equilibrium problems that exhibit symmetric interactions among the agents, which are known to be potential. As may happen in practical cases, however, we envision a scenario in which the formal expression of the underlying potential function is not available, and we design a semi-decentralized Nash equilibrium seeking algorithm. In the proposed two-layer scheme, a coordinator iteratively integrates possibly noisy and sporadic agents’ feedback to learn the pseudo-gradients of the agents, and then design personalized incentives for them. On their side, the agents receive those personalized incentives, compute a solution to an extended game, and then return feedback measurements to the coordinator. In the stationary setting, our algorithm returns a Nash equilibrium in case the coordinator is endowed with standard learning policies, while it returns a Nash equilibrium up to a constant, yet adjustable, error in the time-varying case. As a motivating application, we consider the ride-hailing service provided by several competing companies with mobility as a service orchestration, necessary to both handle competition among firms and avoid traffic congestion.