Self-Assembling of Networks in an Agent-Based Model

We propose a model to show the self-assembling of network-like structures between a set of nodes without using preexisting positional information or long-range attraction of the nodes. The model is based on Brownian agents that are capable of producing different local (chemical) information and respond to it in a non-linear manner. They solve two tasks in parallel: (i) the detection of the appropriate nodes, and (ii) the establishment of stable links between them. We present results of computer simulations that demonstrate the emergence of robust network structures and investigate the connectivity of the network by means of both analytical estimations and computer simulations. PACS: 05.65.+b, 89.75.Kd, 84.30.Bv, 87.18.SnComment: 10 pages, 8 figures. A video of the computer simulations can be found at http://www.ais.fhg.de/~frank/network.html. After publication, this paper was also included in: Virtual Journal of Biological Physics Research 4/5 (September 1, 2002) and Virtual Journal of Nanoscale Science & Technology 6/10 (September 2, 2002). For related work, see also http://www.ais.fhg.de/~frank/active.htm

International crop trade networks: The impact of shocks and cascades

Analyzing available FAO data from 176 countries over 21 years, we observe an increase of complexity in the international trade of maize, rice, soy, and wheat. A larger number of countries play a role as producers or intermediaries, either for trade or food processing. In consequence, we find that the trade networks become more prone to failure cascades caused by exogenous shocks. In our model, countries compensate for demand deficits by imposing export restrictions. To capture these, we construct higher-order trade dependency networks for the different crops and years. These networks reveal hidden dependencies between countries and allow to discuss policy implications