2008/09/26 by Hui He, He, Hui
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #60F17 #60G57 (Primary) #60J80 (Secondary) #60K35 #Complex Systems and Time Series Analysis #Economic theories and models #FOS: Mathematics #Innovation Diffusion and Forecasting #Probability (math.PR) #math.PR #msc:60F17 #msc:60G57 #msc:60J80 #msc:60K35
paper · pdf · doi:10.48550/arxiv.0809.4520
openalex publication_date 2008/09/26 · arxiv created 2009/02/04 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recently, it has been shown that stochastic spatial Lotka-Volterra models when suitably rescaled can converge to a super Brownian motion. We show that the limit process could be a super stable process if the kernel of the underlying motion is in the domain of attraction of a stable law. The corresponding results in Brownian setting were proved by Cox and Perkins (2005, 2008). As applications of the convergence theorems, some new results on the asymptotics of the voter model started from single 1 at the origin are obtained which improve the results by Bramson and Griffeath (1980).