On decentralized computation of the leader's strategy in bi-level games

TL;DR

This paper introduces a decentralized method for computing local Stackelberg equilibria in bi-level games, ensuring convergence and scalability while preserving privacy, with empirical validation in smart mobility scenarios.

Contribution

It presents a novel decentralized gradient-based algorithm with convergence guarantees for bi-level games, including a warm-start procedure to address initialization challenges.

Findings

Proven convergence to local Stackelberg equilibria under broad conditions

Effective handling of convex constraints in bi-level game settings

Successful empirical validation in smart mobility applications

Abstract

Motivated by the omnipresence of hierarchical structures in many real-world applications, this study delves into the intricate realm of bi-level games, with a specific focus on exploring local Stackelberg equilibria as a solution concept. While existing literature offers various methods tailored to specific game structures featuring one leader and multiple followers, a comprehensive framework providing formal convergence guarantees to a local Stackelberg equilibrium appears to be lacking. Drawing inspiration from sensitivity results for nonlinear programs and guided by the imperative to maintain scalability and preserve agent privacy, we propose a decentralized approach based on the projected gradient descent with the Armijo stepsize rule. The main challenge here lies in assuring the existence and well-posedness of Jacobians that describe the leader's decision's influence on the achieved equilibrium of the followers. By meticulous tracking of the Implicit Function Theorem requirements at each iteration, we establish formal convergence guarantees to a local Stackelberg equilibrium for a broad class of bi-level games. Building on our prior work on quadratic aggregative Stackelberg games, we also introduce a decentralized warm-start procedure based on the consensus alternating direction method of multipliers addressing the previously reported initialization issues. Finally, we provide empirical validation through two case studies in smart mobility, showcasing the effectiveness of our general method in handling general convex constraints, and the effectiveness of its extension in tackling initialization issues.