Information Society as a Complex System
Santa Fe Institute pages
Urban Scaling Group
Geoff West et al.
photos - final meeting
Organization of Production Group
David Lane et al.
check out: activity theory
Sander van der Leeuw et al.
Luis Bettencourt Physics, Los Alamos National Lab
Tipping the balances of a small world
From boom to bust and back again: the complex dynamics of trends and fashions
Ole Peters Physics, Imperial College
Rain: Relaxations in the sky
Tim Evans Physics, Imperial College
Ray Rivers Physics, Imperial College
Jose Lobo, Visiting Researcher, Santa Fe Institute, and Assistant Professor of City and Regional Planning: mathematical modeling and quantitative techniques, policy planning, and economic theory. http://www.crp.cornell.edu/home/rs/Default.htm
Regional Science and the "New Economic Geography"
Serra, R., Villani, M. & Semeria, A. (2003): Robustness to damage of biological and synthetic networks. In W.
Banzhaf, T. Christaller, P. Dittrich, J.T. Kim & J. Ziegler (eds): Advances in Artificial Life. Berlin:
Springer Lecture Notes in Artificial Intelligence 2801, 706-715
Serra, R. & Villani, M. (2002): Perturbing the regular topology of cellular automata: implications for the
dynamics. In B. Chopard, M. Tomassini & S. Bandini (eds): Cellular Automata. Berlin: Springer Lecture Notes in
Computer Science 2493, 168-177
Proposal on the table for Doug, Luis and Roberto to collaborate on a simulation to show the governing variables
for the scale-free power-law coefficient for preferential attachment
Development of Method
Node Color = Organizational Form
1 Cyan = Biotechnology Firms
2 Orange = Public Research Organizations
3 Yellow = Large Pharmaceutical/Chemical Corporations
4 Brown = Government Agencies
5 Gray = Venture Capital Firms
6 White = Other Organization
Tie Color = Activity Type
1 Red = Research & Development
2 Green = Finance
3 Blue = Commercialization
5 Magenta = Licensing
Progress in Findings:
The 220 independent cycles among 472 biotech organizations and their partners (2700 organizations) were examined.
As hypothesized, a power-law distribution was found for the frequency histogram (slope =1.3). Outliers, even to this
curve, were found for Biotech-Government-Biotech triads, with all four activity types roughly equal in the contribution
of edges. This shows how Government agencies such as NIH are supernumerary hubs of the network. Such hubs also
produce an unusually high clustering coefficient. Further analysis will break out the histogram of frequencies
of Biotech-Government-Biotech triads by specific agency and type of activities. All 15 combinations occur for the
four types of edges, which indicates agency support for all the permutations of activity types.
The significance of these rough statistical tendencies towards power-law distributions of cycle frequencies is not that
they constitute a scale-invariant process across many orders of magnitude (here there are fewer than two decades) but
that they indicate preferential attachments for particular types of cycles.
Università di Modena e Reggio Emilia
CNRS UMR 7041, Archéologies et Sciences de l' Antiquité, Paris
CNRS UMR 8504 - Géographie-cités èquipe, Paris
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