Complex systems are abundant: The society, the economy, the financial system, food webs, energy supply systems are just some examples. The recent development of information technology opened unprecedented opportunities in studying them. On the one hand due to making available huge amounts of data, on the other hand by offering computer power for simulating them.
Networks represent the scaffold of complex systems, on which the functioning processes take place. Complex systems are heterogeneous in many ways. Broad distributions characterize the network degrees, the link weights and the properties of the nodes as well as aspects of their temporal behavior. Most analytical approaches like those with “representative agents’ simplify to an unrealistic level this relevant multitude of heterogeneities. Therefore simulation techniques, like heterogeneous multi-agent modeling becomes increasingly important.
Agent based modeling is a flexible framework to simulate the action and interaction of entities of complex systems. The course will give an introduction to this topic by making students acquainted with the most important concepts and tools. A versatile, open access easy-to-use platform will be presented, which enables to construct own models and simulations. Relevant agent based models will be discussed from the field of biology, sociology, economics and finance. Issues include conflicts and consensus in opinion formation, segregation as a result of homophily, origins of cultural diversity and market simulations. Techniques of model calibration and validation will be presented.
Learning activities and teaching methods
Class sessions include lectures and exercises. Students will get acquainted with the platform and programing language NetLogo in an interactive way. They have to carry out project work in pairs, which consists of finding a complex problem of interest, define the agent-based model, write the NetLogo program for it, run the program and analyze the results.
N. Gilbert, Agent-Based Models, Sage Publications 2008.
J.H. Miller and S.E. Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press 2007.
C. Castellano, S. Fortunato, and V. Loreto, Statistical physics of social dynamics, Rev.