Traditional software development is based on the closed-world assumption that the boundary between system and environment is known and unchanging. However, this assumption no longer works within today's unpredictable open-world settings, which demands techniques that let software react to changes by self-organizing its structure and self-adapting its behavior.

Small world networks have received disproportionate notice in diverse
elds because of their suspected effect on system dynamics.
e authors analyzed the small world network of the creative artists
o made Broadway musicals from 1945 to 1989. Using original
guments, new statistical methods, and tests of construct validity,
ey found that the varying “small world” properties of the systemic level
twork of these artists affected their creativity in terms of the
nancial and artistic performance of the musicals they produced.
e small world network effect was parabolic; performance
creased up to a threshold, after which point the positive effects
versed.

Inspired by empirical studies of networked systems such as the Internet,
cial networks, and biological networks, researchers have in recent years
veloped a variety of techniques and models to help us understand or predict
e behavior of these systems. Here we review developments in this field,
cluding such concepts as the small-world effect, degree distributions,
ustering, network correlations, random graph models, models of network growth
d preferential attachment, and dynamical processes taking place on networks.