multiplication of Shannon’s information and disequilibrium, in the sense of “far from ther- modynamical equilibrium”, where entropy is maximal (Nicolis and Prigogine, 1977). In this way, highly ordered systems, such as crystals, will have and information close to zero, a high disequilibrium, and thus a low complexity. On the other hand, highly chaotic systems, such as gases, will have a high information, a disequilibrium close to zero (since they are close to thermodynamical equilibrium), and also a low complexity. High complexities are achieved for balanced values of information and disequilibrium. 2.2 Emergence The concept of emergence has been studied for centuries. Intuitively, emergence refers to properties of a higher scale that are not present at the lower scale. For example, the color, conductivity, malleability of gold is not present in the properties of gold atoms (Anderson, 1972). Emergence can be also seen as a change of description or as a metamodel (Heylighen, 1991). There have been several types of emergence proposed (Bedau, 1997; Bar-Yam, 2004a), and there are problems that depend more on a philosophical perspective than on mathemat- ics. Shalizi proposed a measure of efficiency of prediction based on information theory (Shalizi, 2001; Prokopenko et al., 2009) which can be used to study which scale is more efficient for predicting the future. For example, it is useful to speak about emergent thermodynamics, since prediction is more efficient at a macro scale than at a micro scale, where information of individual atoms is taken into account. In another measure of emergence, Holzer and De Meer (2011) compare the information at the system level (sum of all edges in a network) with the information of a lower level (a single edge). This measure gives a high value of emergence for systems with many dependencies (interdependent components) and a low value of emergence for systems with few dependencies (independent components). 6