2016/10/26 by Luca Bortolussi, Cheng Feng
Computer Science · #cs.MA #cs.PF
paper · pdf · doi:10.4204/eptcs.227.3
published as EPTCS 227, 2016, pp. 30-43 · In Proceedings QAPL'16, arXiv:1610.07696
arxiv created 2016/10/26 · arxiv updated 2016/10/27
In this paper we focus on spatial Markov population models, describing the stochastic evolution of populations of agents, explicitly modelling their spatial distribution, representing space as a discrete, finite graph. More specifically, we present a heuristic approach to aggregating spatial locations, which is designed to preserve the dynamical behaviour of the model whilst reducing the computational cost of analysis. Our approach combines stochastic approximation ideas (moment closure, linear noise), with computational statistics (spectral clustering) to obtain an efficient aggregation, which is experimentally shown to be reasonably accurate on two case studies: an instance of epidemic spreading and a London bike sharing scenario.