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.


Markov Chain Cellular Automaton Regular Graph Discrete Space Continuous Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is supported by the EU project QUANTICOL, 600708. The author thanks Jane Hillston and Mieke Massink for their useful comments.


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© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Laboratory for Foundations of Computer Science, School of InformaticsUniversity of EdinburghEdinburghUK

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