Nash equilibrium, as an essential strategic profile in game theory, is of both practical
relevance and theoretical significance due to its wide penetration into various fields, such as …

Convergent sequences of real numbers play a fundamental role in many different problems
in system theory, eg, in Lyapunov stability analysis, as well as in optimization theory and …

Designing privacy-preserving distributed algorithms for stochastic aggregative games is
urgent due to the privacy issues caused by information exchange between players. This …

This paper considers an n-player stochastic Nash equilibrium problem (NEP) in which the i
th player minimizes a composite objective f_i(∙,x_-i)+r_i(∙), where f_i is an expectation-valued …

As an important branch of evolutionary game theory, iterated games describe the situations
that interacting agents play repeatedly based on previous outcomes by using the conditional …

The distributed computation of a Nash equilibrium in aggregative games is gaining
increased traction in recent years. Of particular interest is the coordinator-free scenario …

In this paper we propose an exact, deterministic, and fully continuous reformulation of
generalized Nash games characterized by the presence of soft coupling constraints in the …

This article proposes a new aggregative game (AG) model with feedback delays. The
strategies of players are selected from given strategy sets and subject to global nonlinear …

Generative adversarial networks (GANs) are a class of generative models with two
antagonistic neural networks: a generator and a discriminator. These two neural networks …

This paper tackles the problem of solving stochastic optimization problems with a decision-
dependent distribution in the setting of stochastic strongly-monotone games and when the …