4 Experiments In this section we present computational experiments designed to exemplify the measures proposed above, as well as studying their changes with scale. 4.1 Random Boolean Networks and Elementary Cellular Automata Random Boolean networks (RBNs) were first proposed by Stuart Kauffman as models of ge- netic regulatory networks (Kauffman, 1969, 1993) and have been extensively studied (Wuen- sche, 1998; Gershenson, 2002a; Aldana-Gonz´ alez et al., 2003; Gershenson, 2004a; Drossel, 2009; Gershenson, In Pressa). A RBN consists of N Boolean nodes, i.e. they can take values of 0 or 1. The state of each node is determined by K other nodes (on average). The structure of the RBN is determined by the network of interacting nodes. The function of the RBN is given by lookup tables that determine the future state of each node depending on the current state of its inputs. The structure and the function of RBNs are initially generated randomly and remain fixed afterwards. Classic RBNs are updated synchronously (Gershenson, 2004c) and thus their dynamics are deterministic. Since their state space is finite (2 N states), sooner or later a state will be repeated. When this occurs, the RBN has reached an attractor. These can be point attractors (a single state) or cycle attractors of different lengths. Depending on different structural and functional properties, RBN dynamics can be or- dered, chaotic, or critical (Gershenson, In Pressa). Ordered dynamics are characterized by few nodes changing and high robustness. Chaotic dynamics are characterized by most nodes changing and high fragility. Critical dynamics balance the variability of the chaotic regime with the robustness of the ordered regime. Critical dynamics are related to a high complexity. Elementary cellular automata (ECA) have also been studied extensively (Wolfram, 1984; Wuensche and Lesser, 1992; Wolfram, 2002). They can be seen as particular cases of 13