I = − $^{∑}$P ( x ) log P ( x ) . (1) For binary strings, the logarithm is usually taken with base two. For example, if the probabil- ity of receiving ones is maximal ( P (1) = 1) and the probability of receiving zeros is minimal ( P (0) = 0), the information is minimal, i.e. I = 0, since we know beforehand that the future value of x will be 1. There is no information because future values of x do not add novel information, i.e. the values are known beforehand. If we have no knowledge about the future value of x , as with a fair coin toss, then P (0) = P (1) = 0 . 5. In this case, information will be maximal, i.e. I = 1, because a future observation will give us all the relevant information, which is also independent of previous values. Equation 1 is plotted in Figure 1. Shannon information can be seen also as a measure of uncertainty. If there is absolute certainty about the future of x , be it zero ( P (0) = 1) or one ( P (1) = 1), then the information received will be zero. If there is no certainty due to the probability distribution ( P (0) = P (1) = 0 . 5), then the information received will be maximal. The unit of information is the bit. One bit represents the information gained when a binary random variable becomes known. If no information is gained, then I = 0 bits. Prokopenko et al. (2009) have produced a primer where proposals of measures of com- plexity, emergence, and self-organization based on information theory are reviewed. Their primer is much more extensive and detailed than this section. 2.1 Complexity There are dozens of notions, definitions, and measures of complexity (Edmonds, 1999; Lloyd, 2001). Etymologically, the term complexity comes from the Latin plexus , which means interwoven. In other words, something complex is difficult to separate. This can be said to be because of relevant interactions between components (Gershenson, In Pressb). However, when an observation is made in terms of information, a relevant question is “how complex 4