Session
Computer Science and Communication Engineering
Description
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.
Keywords:
Agent-Based Model, Markov Chain, Random Map Representation
Proceedings Editor
Edmond Hajrizi
ISBN
978-9951-550-14-7
First Page
166
Last Page
170
Location
Durres, Albania
Start Date
7-11-2015 9:00 AM
End Date
7-11-2015 5:00 PM
DOI
10.33107/ubt-ic.2015.105
Recommended Citation
Kitzler, Florian and Bicher, Martin, "Case Studies for a Markov Chain Approach to Analyze Agent-Based Models" (2015). UBT International Conference. 105.
https://knowledgecenter.ubt-uni.net/conference/2015/all-events/105
Case Studies for a Markov Chain Approach to Analyze Agent-Based Models
Durres, Albania
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.