Agent-Based Model, Markov Chain, Random Map Representation
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.
Kitzler, Florian and Bicher, Martin
"Case Studies for a Markov Chain Approach to Analyze Agent-Based Models,"
International Journal of Business and Technology: Vol. 4
, Article 6.
Available at: https://knowledgecenter.ubt-uni.net/ijbte/vol4/iss1/6