A particle system is said to be non-exchangeable if two particles cannot be exchanged
without modifying the overall dynamics. Because of this property, the classical mean-field …

The mean-field analysis of a multipopulation agent-based model is performed. The model
couples a particle dynamics driven by a nonlocal velocity with a Markov-type jump process …

Interacting particle systems are known for their ability to generate large-scale self-organized
structures from simple local interaction rules between each agent and its neighbors. In …

Motivated by a general principle governing regulation mechanisms in biological cells, we
investigate a general interaction scheme between different populations of particles and …

The fixed-point analysis refers to the study of fixed-points that arise in the context of complex
systems with many interacting entities. In this expository paper, we describe four levels of …

We study a population of N particles, which evolve according to a diffusion process and
interact through a dynamical network. In turn, the evolution of the network is coupled to the …

This paper investigates the limit behavior of Markov Decision Processes (MDPs) made of
independent particles evolving in a common environment, when the number of particles …

We study a system consisting of $ n $ particles, moving forward in jumps on the real line.
Each particle can make both independent jumps, whose sizes have some distribution …

We study a system of $ N $ interacting particles on $\bf {Z} $. The stochastic dynamics
consists of two components: a free motion of each particle (independent random walks) and …

We study the limiting behaviour of stochastic models of populations of interacting agents, as
the number of agents goes to infinity. Classical mean-field results have established that this …