  The Kauffman model describes a system of randomly connected nodes with
dynamics based on Boolean update functions. Though it is a simple model, it
exhibits very complex behavior for "critical" parameter values at the boundary
between a frozen and a disordered phase, and is therefore used for studies of
real network problems. We prove here that the mean number and mean length of
attractors in critical random Boolean networks with connectivity one both
increase faster than any power law with network size. We derive these results
by generating the networks through a growth process and by calculating lower
bounds.
