REVIEW 5 cited by
Random resetting in search problems
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
By periodically returning a search process to a known or random state, random resetting possesses the potential to unveil new trajectories, sidestep potential obstacles, and consequently enhance the efficiency of locating desired targets. In this chapter, we highlight the pivotal theoretical contributions that have enriched our understanding of random resetting within an abundance of stochastic processes, ranging from standard diffusion to its fractional counterpart. We also touch upon the general criteria required for resetting to improve the search process, particularly when distribution describing the time needed to reach the target is broader compared to a normal one. Building on this foundation, we delve into real-world applications where resetting optimizes the efficiency of reaching the desired outcome, spanning topics from home range search, ion transport to the intricate dynamics of income. Conclusively, the results presented in this chapter offer a cohesive perspective on the multifaceted influence of random resetting across diverse fields.
Forward citations
Cited by 5 Pith papers
-
A resetting particle embedded in a viscoelastic bath
A particle in a viscoelastic bath under stochastic resetting has exact renewal formulas for its mean-square displacement and autocorrelation, with explicit Jeffreys-fluid results.
-
Occupation time statistics for non-Markovian random walks
Derives Feynman-Kac equations for occupation time statistics of continuous-time random walks with arbitrary waiting times, recovering arcsine and Lamperti distributions and adding resetting.
-
The impact of stochastic resetting on resource allocation: The case of Reallocating geometric Brownian motion
Resetting an unstable resource-redistribution model frequently enough makes its mean and variance converge, turning a non-ergodic process into a stationary one.
-
Stochastic Compartment Model of Epidemic Spreading in Complex Networks with Mortality and Resetting
A stochastic SEIRD epidemic model on networks with random waiting times and stochastic resetting predicts endemic states for R0 > 1, and simulations show resetting raises R0.
-
From random walks to epidemic spreading: Compartment model with mortality for vector transmitted diseases
A mean-field model with mortality for vector-borne disease on networks yields a reproduction number R_M that is always at most R_0, with an endemic equilibrium when R_0 > 1.
Discussion (0). Continue with ORCID to comment.