Divided over three exhibition projects taken place in Sao Paulo, Barcelona, and Amsterdam, Beltrán interviewed a large number of people and collected a variety of personal theories on all kind of subjects. He drew his inspiration from micro-history, a genre in cultural history that focuses on personal stories and apparently minor events, sketching a picture of a culture or mentality of a particular period. ‘Our view of the world is determined not just by what we have learned about the world or even what we have actually experienced,’ Beltrán explains. ‘It consists to a large extent of suspicions, makeshift connections and personal interpretations.’
Collective dynamics of ‘small-world’ networks Duncan J. Watts* & Steven H. Strogatz Department of Theoretical and Applied Mechanics, Kimball Hall, Cornell University, Ithaca, New York 14853, USA
Networks of coupled dynamical systems have been used to model biological oscillators1–4, Josephson junction arrays5,6, excitable media7, neural networks8–10, spatial games11, genetic control networks12 and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them ‘small-world’ networks, by analogy with the small-world phenomenon13,14 (popularly known as six degrees of separation15). The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.