Computer Engineering Commons^™

Case Studies For A Markov Chain Approach To Analyze Agent-Based Models, Florian Kitzler, Martin Bicher

UBT International Conference

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 …