Long Time Asymptotics for the Stochastic Follow-the-Leader System

TL;DR

This paper introduces a stochastic particle system combining leader-follower dynamics with distance-dependent interactions, establishing ergodicity, explicit stationary laws, and analyzing mixing times and fluctuations.

Contribution

It provides the first rigorous analysis of a stochastic follow-the-leader model with explicit stationary distributions and detailed mixing time estimates.

Findings

Existence of a unique stationary distribution for the gap process.

Explicit stationary law as a product of exponential distributions when leader's jumps are exponential.

Mixing time scales between linear and near-quadratic in the number of particles.

Abstract

We introduce and analyze a class of interacting particle systems on the real line that combine features of the stochastic rat race and (deterministic) follow-the-leader models. The particle system evolves as a continuous-time pure jump process: the leading particle moves independently, at Exponential jump times, with constant jump rate and iid jump sizes distributed according to a law $\theta$, while each of the remaining particles jumps forward, at Exponential times, at rate equal to its distance from the particle immediately ahead, with jump sizes drawn uniformly from the corresponding gap. The dynamics thus encode competition for leadership together with distance-dependent stochastic interactions. Our main focus is the associated gap process, representing the vector of inter-particle distances. We establish the existence of a unique stationary distribution for the gap process and prove uniform geometric ergodicity. Further, when the leader's jump sizes follow an Exponential distribution, we identify the stationary law explicitly as a product of independent Exponential laws, and show that the associated mixing time scales between $\Theta(n)$ and $O(n(\log n)^2)$ for an $n$-particle system. As an application of the mixing time results we establish a functional limit theorem that characterizes fluctuations of particle states at large time, under a suitable spatial and temporal scaling and large particle limit. Finally, when the leader's jumps have heavy but integrable tails, we show that each gap has at least one additional finite moment under stationarity than that of the leader's jump size distribution. The model offers a tractable setting for exploring ergodicity, explicit invariant laws, and mixing behavior in non-diffusive particle systems.