Exploring synthetic mass action models

Abstract

In this work we propose a model that can be used to study the dynamics of mass action systems, systems consisting of a large number of individuals whose behavior is influenced by other individuals that they encounter. Our approach is rather synthetic and abstract, viewing each individual as a probabilistic automaton that can be in one of finitely many discrete states. We demonstrate the type of investigations that can be carried out on such a model using the Populus toolkit. In particular, we illustrate how sensitivity to initial spatial distribution can be observed in simulation.