Stochastic Real-Time Deception in Nash Equilibrium Seeking for Games with Quadratic Payoffs

TL;DR

This paper introduces a stochastic deception strategy in Nash equilibrium seeking for quadratic payoff games, enabling deceptive players to influence outcomes by exploiting real-time information, with proven local exponential convergence.

Contribution

It presents a novel stochastic deception approach in model-free Nash equilibrium seeking, specifically for quadratic games, with convergence analysis and practical illustration.

Findings

Deceptive players can steer the system to advantageous equilibria.

The proposed method achieves local exponential convergence.

Application to a two-player quadratic game demonstrates effectiveness.

Abstract

In multi-agent autonomous systems, deception is a fundamental concept which characterizes the exploitation of unbalanced information to mislead victims into choosing oblivious actions. This effectively alters the system's long term behavior, leading to outcomes that may be beneficial to the deceiver but detrimental to victim. We study this phenomenon for a class of model-free Nash equilibrium seeking (NES) where players implement independent stochastic exploration signals to learn the pseudogradient flow. In particular, we show that deceptive players who obtain real-time measurements of other players' stochastic perturbation can incorporate this information into their own NES action update, consequentially steering the overall dynamics to a new operating point that could potentially improve the payoffs of the deceptive players. We consider games with quadratic payoff functions, as this restriction allows us to derive a more explicit formulation of the capabilities of the deceptive players. By leveraging results on multi-input stochastic averaging for dynamical systems, we establish local exponential (in probability) convergence for the proposed deceptive NES dynamics. To illustrate our results, we apply them to a two player quadratic game.