# Fixed points with finite mean of the smoothing transform in random environments

## TL;DR

This paper investigates the existence and uniqueness of fixed points with finite mean for the smoothing transform in random environments, extending classical results and applying findings to branching random walks.

## Contribution

It extends classical fixed point results of the smoothing transform to the setting of random environments, providing new conditions for existence and uniqueness.

## Key findings

- Established conditions for fixed points with finite mean in random environments.
- Proved the martingale convergence of branching random walks in random environments.
- Extended Biggins' classical results to a more general setting.

## Abstract

At each time $n\in\mathbb{N}$, let $\bar{Y}^{(n)}=(y_{1}^{(n)},y_{2}^{(n)},\cdots)$ be a random sequence of non-negative numbers that are ultimately zero in a random environment $\xi=(\xi_{n})_{n\in\mathbb{N}}$ in time, which satisfies for each $n\in\mathbb{N}$ and a.e. $\xi,~E_{\xi}[\sum_{i\in\mathbb{N}_{+}}y_{i}^{(n)}(\xi)]=1.$ The existence and uniqueness of the non-negative fixed points of the associated smoothing transform in random environments is considered. These fixed points are solutions of the distributional equation for $a.e.~\xi,~Z(\xi)\overset{d}{=}\sum_{i\in\mathbb{N}_{+}}y_{i}^{(0)}(\xi)Z_{i}(T\xi),$ where when given the environment $\xi$, $Z_{i}(T\xi)~(i\in\mathbb{N}_{+})$ are $i.i.d.$ non-negative random variables, and distributed the same as $Z(\xi)$. As an application, the martingale convergence of the branching random walk in random environments is given as well. The classical results by Biggins (1977) has been extended to the random environment situation.