DeepAI

# Stochastic generalized Nash equilibrium seeking under partial-decision information

We consider for the first time a stochastic generalized Nash equilibrium problem, i.e., with expected-value cost functions and joint feasibility constraints, under partial-decision information, meaning that the agents communicate only with some trusted neighbours. We propose several distributed algorithms for network games and aggregative games that we show being special instances of a preconditioned forward-backward splitting method. We prove that the algorithms converge to a generalized Nash equilibrium when the forward operator is restricted cocoercive by using the stochastic approximation scheme with variance reduction to estimate the expected value of the pseudogradient.

• 13 publications
• 39 publications
10/25/2019

### A damped forward-backward algorithm for stochastic generalized Nash equilibrium seeking

We consider a stochastic generalized Nash equilibrium problem (GNEP) wit...
05/31/2020

### Quantization Games on Social Networks and Language Evolution

We consider a strategic network quantizer design setting where agents mu...
09/14/2019

### Acquisition Games with Partial-Asymmetric Information

We consider an example of stochastic games with partial, asymmetric and ...
06/10/2022

### The Generalized Eigenvalue Problem as a Nash Equilibrium

The generalized eigenvalue problem (GEP) is a fundamental concept in num...
05/02/2022

### Hierarchical Decompositions of Stochastic Pursuit-Evasion Games

In this work we present a hierarchical framework for solving discrete st...
07/24/2019

### Infection-Curing Games over Polya Contagion Networks

We investigate infection-curing games on a network epidemics model based...
02/01/2021

### Stochastic Alignment Processes

The tendency to align to others is inherent to social behavior, includin...