Open AccessCase Studies for a Markov Chain Approach to Analyze Agent-Based Models
Author(s) -
Florian Kitzler,
Martin Bicher
Publication year - 2015
Publication title -
international journal of business and technology
Language(s) - English
Resource type - Journals
ISSN - 2223-8387
DOI - 10.33107/ijbte.2015.4.1.06
Subject(s) - flexibility (engineering) , computer science , markov chain , random walk , focus (optics) , representation (politics) , management science , perspective (graphical) , data science , theoretical computer science , machine learning , artificial intelligence , mathematics , engineering , statistics , physics , politics , law , political science , optics
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.