  The Kauffman model describes a particularly simple class of random Boolean
networks. Despite the simplicity of the model, it exhibits complex behavior and
has been suggested as a model for real world network problems. We introduce a
novel approach to analyzing attractors in random Boolean networks, and applying
it to Kauffman networks we prove that the average number of attractors grows
faster than any power law with system size.
