Location Aggregation of Spatial Population CTMC Models

Luca Bortolussi
(University of Trieste, CNR-ISTI)
Cheng Feng
(University of Edinburgh)

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.

In Mirco Tribastone and Herbert Wiklicky: Proceedings 14th International Workshop Quantitative Aspects of Programming Languages and Systems (QAPL'16), Eindhoven, The Netherlands, April 2-3, 2016, Electronic Proceedings in Theoretical Computer Science 227, pp. 30–43.
Published: 25th October 2016.

ArXived at: https://dx.doi.org/10.4204/EPTCS.227.3 bibtex PDF
References in reconstructed bibtex, XML and HTML format (approximated).
Comments and questions to: eptcs@eptcs.org
For website issues: webmaster@eptcs.org