Making stochastic process algebras count: modelling collective dynamics

Stochastic process algebras have been successfully applied to quantitative
evaluation of systems for over a decade. Such models may be used to
analyse the timeliness of response or the utilisation of resources of systems.

By modelling systems as collections of individual agents, the process
algebra approach allows the modeller to capture the exact form of
interactions and constraints between entities. At any given time the
state of the system is the collection of states exhibited by the individual
agents. Inevitably this approach suffers from the problem of state space
explosion making analysis inefficient or even infeasible, particularly in
systems where we are interested in collective dynamics. In these systems,
although we model the behaviour of individuals, we aim to analyse the
behaviour of the populations to which they belong. Examples include clients
accessing a server, people moving through their physical environment, or
molecules interacting within cells.

In this talk I will discuss recent work on such population-oriented
models described in stochastic process algebra.