Contributions to Proceedings:
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.
English abstract:
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.
Electronic version of the publication:
http://publik.tuwien.ac.at/files/PubDat_245657.pdf
Created from the Publication Database of the Vienna University of Technology.