Random walk with chaotically driven bias

Song Ju Kim, Makoto Naruse, Masashi Aono, Hirokazu Hori, Takuma Akimoto

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


We investigate two types of random walks with a fluctuating probability (bias) in which the random walker jumps to the right. One is a time-quenched framework' using bias time series such as periodic, quasi-periodic, and chaotic time series (chaotically driven bias). The other is a time-annealed framework' using the fluctuating bias generated by a stochastic process, which is not quenched in time. We show that the diffusive properties in the time-quenched framework can be characterised by the ensemble average of the time-averaged variance (ETVAR), whereas the ensemble average of the time-averaged mean square displacement (ETMSD) fails to capture the diffusion, even when the total bias is zero. We demonstrate that the ETVAR increases linearly with time, and the diffusion coefficient can be estimated by the time average of the local diffusion coefficient. In the time-annealed framework, we analytically and numerically show normal diffusion and superdiffusion, similar to the Lévy walk. Our findings will lead to new developments in information and communication technologies, such as efficient energy transfer for information propagation and quick solution searching.

Original languageEnglish
Article number38634
JournalScientific reports
Publication statusPublished - 2016 Dec 8
Externally publishedYes

ASJC Scopus subject areas

  • General


Dive into the research topics of 'Random walk with chaotically driven bias'. Together they form a unique fingerprint.

Cite this