Superpolynomial Growth in the Number of Attractors in Kaufman Networks

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. This work is based on an earlier paper where we introduced a novel approach to analyzing attractors in random Boolean networks. Applying this approach to Kauffman networks, we prove that the average number of attractors grows faster than any power law with system size. (author)