Complex systems, Computer science, Political science, Sociology

      Co-sponsored by AITIA International

Course date

13 July - 24 July, 2009
Application deadline
15 February, 2009
Course Director(s): 

Laszlo Gulyas

Collegium Budapest / ELTE, Department of History and Philosophy of Science, Hungary

Gyorgy Kampis

Collegium Budapest, Focus Group on the Philosophy of Complexity / ELTE, Department of History and Philosophy of Science, Hungary
Course Faculty: 

Petra Ahrweiler

Innovation Research Unit, CASL, University College Dublin (UCD), Ireland

Peter Erdi

Department of Biophysics, KFKI Research Institute for Particle and Nuclear Physics of the Hungarian Academy of Sciences

Robert Goldstone

Department of Psychological and Brain Sciences/Program in Cognitive Science, Indiana University, USA

Ferenc Jordan

Animal Ecology Research Group of the Hungarian Academy of Sciences (HAS), Budapest, Hungary

Shu-Heng Chen

AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, Taiwan

Klaus G. Troitzsch

University of Koblenz-Landau, Institute for IS Research, Germany

The summer school is aimed at providing a state-of-the-art cutting-edge scientific and research-oriented training for junior faculty, young researchers, postdoctoral fellows, MA and Ph.D. students, and professionals from European and overseas universities and research institutes on complex systems and social simulations.

The term Complex Systems (CSS) denotes an interdisciplinary research methodology currently successful in the social sciences and elsewhere. CS research originated from physics and nonlinear systems some decades ago but its models have soon permeated such distant fields as economy, political science or more recently sociology. As implied by the name, a CS is essentially a system of many complicated interactions. Complex Systems methodology has developed sophisticated yet well understood tools to cope with this challenge. In social systems the essence of CS is the characterization of the distributed dynamics of how the interaction of many actors and variables leads to predictable phenomena, which often involve hierarchy, emergence, dynamic structures and large scale transitions.

Each day in the course focuses on one tool of this encompassing methodology. CS methods include various mathematical models (nonlinear systems, networks, statistical approaches), computer simulations (e.g. systems dynamics, agent-based modeling). CS simulations are highly computation intensive and pose problems of supercomputing and parallelization.

The CSSS course offers lectures, tutorials and discussions on the whole spectrum of the above. Lectures are from leading experts, specifically focusing on CS concepts, modeling and (social) simulation, followed by discussion.