Spatial Representations and Analysis Techniques

Abstract

Abstract. Space plays an important role in the dynamics of collective adaptive systems (CAS). There are choices between representations to be made when we model these systems with space included explicitly, rather than being abstracted away. Since CAS often involve a large number of agents or components, we focus on scalable modelling and analysis of these models, which may involve approximation techniques. Discrete and continuous space are considered, for both models of individuals and models of populations. The aim of this tutorial is to provide an overview that supports decisions in modelling systems that involve space.

Modelling and Analysis of Collective Adaptive Systems with CARMA and its Tools

“…The language offers a rich set of communication primitives, and the exploitation of attributes, captured in a store associated with each component, to enable attribute-based communication. For most CAS systems we anticipate that one of the attributes could be the location of the agent [15]. Thus it is straightforward to model those systems in which, for example, there is limited scope of communication or, restriction to only interact with components that are co-located, or where there is spatial heterogeneity in the behaviour of agents.…”

Mean-Field Limits Beyond Ordinary Differential Equations

“…We start in Section 2.1 by introducing a framework to describe the class of systems amenable of mean field analysis, namely Markov population processes (see also the chapter on spatial representations [26]). We illustrate these concepts in Section 2.2 by means of a classic epidemic spreading model.…”