Personalized incentives as feedback design in generalized Nash equilibrium problems

TL;DR

This paper introduces a semi-decentralized algorithm for finding Nash equilibria in potential games with symmetric interactions, especially when the potential function is unknown, using personalized incentives and feedback.

Contribution

It proposes a novel two-layer scheme that learns pseudo-gradients and designs incentives without explicit potential function knowledge, applicable to stationary and time-varying scenarios.

Findings

Algorithm converges to Nash equilibrium in stationary cases.

Achieves approximate equilibrium in time-varying settings.

Validated through numerical experiments in ridehailing services.

Abstract

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 the (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 ridehailing service provided by several companies with mobility as a service orchestration, necessary to both handle competition among firms and avoid traffic congestion, which is also adopted to run numerical experiments verifying our results.