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F. Kitzler, M. Bicher:
"Case Studies for a Markov Chain Approach to Analyze Agent-Based Models";
in: "International Conference on Business, Technology and Innovation ICBTI 2015", 1; E. Hajrizi (ed.); issued by: University of Business and Technology, Pristina; UBT - Higher Education Institution, Durres, Albanien, 2015, ISBN: 978-9951-437-36-3, 55 - 56.

Agent-based models have become a widely used tool in social sciences, health care management and other disciplines to describe complex systems from a bottom-up perspective. Some reasons for that are the easy understanding of agent-based models, the high flexibility and the possibility to describe heterogeneous structures. Nevertheless problems occur when it comes to analyzing agent-based models.
This paper shows how to describe agent-based models in a macroscopic way as Markov chains, using the random map representation. The focus is on the implementation of this method for chosen examples of a Random Walk and Opinion Dynamic Models. It is also shown how to use Markov chain tools to analyze these models. Our case studies imply that this method can be a powerful tool when it comes to analyzing agent-based models although some further research in practice is still necessary.

http://publik.tuwien.ac.at/files/PubDat_245657.pdf