Random walks with echoed steps I
Daniela Portillo del Valle

TL;DR
This paper introduces a new type of random walk with memory, called RWES, which generalizes previous models and exhibits phase transitions and super-linear scaling, with detailed convergence and distributional results.
Contribution
It defines the RWES model, analyzes its convergence properties, phase transitions, and distributional limits, extending prior random walk models with memory.
Findings
Phase transition at p*E[ξ]=1.
Super-linear scaling in the super-critical regime.
Convergence to random series and distributional properties.
Abstract
A random walk with echoed steps (RWES) is a process that inserts memory and echo into an ordinary random walk (ORW) with i.i.d. steps, . The RWES is defined recursively as follows. Let . With probability , the -th increment of the RWES follows that of the ORW, . Otherwise, is set as a random echo of a uniform sample of the past steps determined by a random factor . Namely, with probability , where Uniform. The RWES is a broad generalization of the elephant random walk and of the positively/negatively/unbalanced step-reinforced random walks. We determine strong convergences of when the echo law is…
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