Scaling in ordered and critical random Boolean networks
  Random Boolean networks, originally invented as models of genetic regulatory networks, are simple models for a broad class of complex systems that show rich dynamical structures. From a biological perspective, the most interesting networks lie at or near a critical point in parameter space that divides ``ordered'' from ``chaotic'' attractor dynamics. In the ordered regime, we show rigorously that the average number of relevant nodes (the ones that determine the attractor dynamics) remains constant with increasing system size N. For critical networks, our analysis and numerical results show that the number of relevant nodes scales like N^{1/3}. Numerical experiments also show that the median number of attractors in critical networks grows faster than linearly with N. The calculations explain why the correct asymptotic scaling is observed only for very large N. 
Joshua E. S. Socolar (Duke University, Durham, NC) and Stuart A.   Kauffman (Bios Group, Santa Fe, NM)