Fellow-Klasse 2010/11 Prof. Dr. Johannes Glückler

Arbeitsvorhaben am Marsilius-Kolleg

Networks - How connectivity shapes spatial economies

Motivation: Linking unconnected expertise in network research

Beyond the manifold metaphorical uses of the term network, network analysis has now become a pan-disciplinary methodological approach. Instead of analyzing empirical networks with traditional attribute-based methods, they are now given full appreciation by applying relational methodologies. Within the framework of the so-called anticategorical imperative (Emirbayer and Goodwin 1994, p. 1414) these methods and algorithms are developed in distinct disciplines such as mathematics, physics, sociology etc. Both, its transdisciplinarity and its booming prominence in scienticfic research indicate the potential value that network theory and methods offer for a dialogue between natural and social sciences. However, over the last years it has become obvious that these two streams of research - methodology (physics and mathematics) and empirics are growing individually: they seem to specialize in different and often unconnected directions. A Marsilius Fellowship offers the ideal environment to exchange findings, questions and open challenges between the fields and to develop new focus and perspectives for joint research proposals in the future. Specifically, many disciplines today celebrate "spatial turns" in their analysis, i.e. they focus on the constraints of physical proximity on how economy and society organize. More generally the social sciences are taking a strong interest in the specific geographical conditions of the economy and our society.

Aims and key questions

The aim of this project is to explore concepts and solutions to important questions such as the following: what are appropriate network strategies to yield collective returns from cooperation? Do all social networks display small world characteristics? What does growth mean for social networks? How do networks contribute and respond to network ecologies (an entirely new field of research)? How do networks evolve over time and what can regional policy learn from network dynamics? Apart from these broad questions there is a more specific question of immediate concern in the cooperation with my above-mentioned colleagues: How can we measure the vulnerability of knowledge networks? And what would be an optimal degree of robustness of such a network? How do network topologies, which are governed by connectivity, respond to network geometry, which is governed by proximity and distance?

Research experience: the economic geography group

My group is working on several projects that deal with the economic benefits and geographical constraints on social networks in and between firms. These projects deal with structural elements, evolutionary dynamics and implications for corporate strategy and regional economic development. Recent examples of our studies on empirical social networks include network architectures and governance for small and medium sized firms (BMBF grant), global corporate knowledge transfer (DFG grant), network strategies of firm internationalization (DFG grant), and academia as network from a the perspective of the sociology of science.

Commitment to interdisciplinary cooperation

This project is fully committed to a tight cooperation with Professor Hamprecht and Professor Reinelt. We have had repeated meetings and opportunities to identify broad joint interests from integrating our research efforts. This cooperation will benefit from a unique intersection of competencies ranging from mathematical algorithms to rich network data which are rooted deeply in empirical social science research. I hope to explore existing algorithms with networkempirics and to identify new empirical problems or situations that inspire modeling in mathematics and informatics. By bringing together distinct perspectives, concepts and methods and by being able to apply network algorithms and models to empirical network data we hope to find new insights into the network paradigm.