Frank Schweitzer

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Full Professor for Systems Design at Swiss Federal Institute of Technology Zurich (formerly ETHZ) ETH Zurich since 2004. He is also associated member of the Department of Physics at the ETH Zurich.

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Editor-in-Chief, "Advances in Complex Systems" (ACS)

Editor-in-Chief, "European Physical Journal B: Condensed Matter and Complex Systems" (EPJB) with responsibility for the section "Complex Systems"

2009 Frank Schweitzer, Didier Sornette, A. Vespignani, G. Fagiolo, F. Vega-Redondo, D. R. White. Economic Networks: The New Challenges. Science 24 July: 422-425. Special Issue on Complex Systems and Networks http://www.sciencemag.org/cgi/reprint/325/5939/422.pdf.

Schweitzer and colleagues present their research into the structure and dynamics of economic networks and the problems of finding a complex systems approach to facilitate the design of policies. The research into economic networks has been studied from two perspectives: more micro approaches from economics or sociology and more macro approaches from physics or computer science. The micro approaches focus on individual system elements and their detailed network of relations. Game theory is often used to model the dynamics of agent behavior. The context of behavior may be modeled by the more macro approaches. These focus on the statistical regularities of the network as a whole but taken alone would fail to link to the economic motivation of individual agents. Combinations of these approaches may describe what is needed in the challenges of predicting stabilities and instabilities and propose economic policies. With computational models, large-scale network data can be processed and can reflect complex agent interactions.

Synopsis of Economic Networks: The New Challenges

Alessandro Vespignani

2009 Frank Schweitzer, Didier Sornette, G. Fagiolo, F. Vega-Redondo, D. R. White. Economic Networks: What do we know and what do we need to know? Advances in Complex Systems (ACS) 12(4-5):407-422. Preprint: Santa Fe Institute Working Paper.

Abstract: We examine the emergent field of economic networks and explore its ability to shed light on the global and volatile economy where credit, ownership, innovation, investment, and virtually every other economic activity is carried at a scale and scope that respects no geographical, organizational, or political boundaries. In this context, the study of economic networks and their dynamics must reflect the vast complexity of the interaction patterns and integrate it with a realistic account of the incentives and information that govern agents' behavior. The interplay of both has been shown to produce metastabilities, system crashes, and emergent structures in ways that are yet only poorly understood. Meeting this exciting scientific challenge requires a combination of time series analysis, complexity theory, and simulation with the analytical tools that have been developed by game theory, as well as graph and matrix theories. We argue that this will help achieving a better integration of theory and data models and provide a better understanding of the potentials and risks of modern economic systems.